Transcutaneous analyte sensor

ABSTRACT

The present invention relates generally to systems and methods for measuring an analyte in a host. More particularly, the present invention relates to systems and methods for transcutaneous measurement of glucose in a host.

RELATED APPLICATIONS

This application is a continuation-in-part of U.S. application Ser. No.11/334,876 filed Jan. 18, 2006 now U.S. Pat. No. 7,774,145. U.S.application Ser. No. 11/334,876 is a continuation-in-part of U.S.application Ser. No. 10/633,367 filed Aug. 1, 2003 now U.S. Pat. No.7,778,680. U.S. application Ser. No. 11/334,876 is acontinuation-in-part of U.S. application Ser. No. 11/007,920 filed Dec.8, 2004, which claims priority under 35 U.S.C. §119(e) to U.S.Provisional Application No. 60/528,382 filed Dec. 9, 2003, U.S.Provisional Application No. 60/587,787 filed Jul. 13, 2004, and U.S.Provisional Application No. 60/614,683 filed Sep. 30, 2004. U.S.application Ser. No. 11/334,876 is a continuation-in-part of U.S.application Ser. No. 10/991,966 filed Nov. 17, 2004 now U.S. Pat. No.7,519,408, which claims priority under 35 U.S.C. §119(e) to U.S.Provisional Application No. 60/523,840 filed Nov. 19, 2003, U.S.Provisional Application No. 60/587,787 filed Jul. 13, 2004, and U.S.Provisional Application No. 60/614,683 filed Sep. 30, 2004. U.S.application Ser. No. 11/334,876 is a continuation-in-part of U.S.application Ser. No. 11/077,715 filed Mar. 10, 2005 now U.S. Pat. No.7,497,827, which claims priority under 35 U.S.C. §119(e) to the U.S.Provisional No. 60/587,787 filed on Jul. 13, 2004, U.S. ProvisionalApplication No. 60/587,800 filed Jul. 13, 2004, U.S. Provisional No.60/614,683 filed Sep. 30, 2004, and U.S. Provisional Application No.60/614,764 filed Sep. 30, 2004. Each of the above-referencedapplications is hereby incorporated by reference herein in its entirety,and each of the above-referenced applications is hereby made a part ofthis specification.

FIELD OF THE INVENTION

The present invention relates generally to systems and methods formeasuring an analyte in a host. More particularly, the present inventionrelates to systems and methods for transcutaneous measurement of glucosein a host.

BACKGROUND OF THE INVENTION

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high blood sugar, which can cause anarray of physiological derangements associated with the deterioration ofsmall blood vessels, for example, kidney failure, skin ulcers, orbleeding into the vitreous of the eye. A hypoglycemic reaction (lowblood sugar) can be induced by an inadvertent overdose of insulin, orafter a normal dose of insulin or glucose-lowering agent accompanied byextraordinary exercise or insufficient food intake.

Conventionally, a person with diabetes carries a self-monitoring bloodglucose (SMBG) monitor, which typically requires uncomfortable fingerpricking methods. Due to the lack of comfort and convenience, a personwith diabetes normally only measures his or her glucose levels two tofour times per day. Unfortunately, such time intervals are so far spreadapart that the person with diabetes likely finds out too late of ahyperglycemic or hypoglycemic condition, sometimes incurring dangerousside effects. It is not only unlikely that a person with diabetes willtake a timely SMBG value, it is also likely that he or she will not knowif his or her blood glucose value is going up (higher) or down (lower)based on conventional method. This inhibits the ability to make educatedinsulin therapy decisions.

SUMMARY OF THE INVENTION

In a first aspect, a system for monitoring a glucose concentration in ahost is provided, the system comprising a continuous glucose sensorconfigured to produce a signal indicative of a glucose concentration ina host; and a receiver operably connected to the sensor, wherein thereceiver comprises a user interface, and wherein the receiver furthercomprises programming configured to calibrate the signal, to display agraphical representation of the calibrated signal on the user interface,and to display a directional arrow indicative of a direction and a rateof change of the calibrated signal on the user interface.

In an embodiment of the first aspect, the receiver comprises programmingconfigured to display the direction and the rate of change of thecalibrated signal by a rotation of the directional arrow with aresolution of from about 1 degree to about 45 degrees.

In an embodiment of the first aspect, the directional arrow isindicative of a direction and a rate of change over a predetermined timeperiod.

In an embodiment of the first aspect, predetermined time period is atleast about 15 minutes.

In an embodiment of the first aspect, the receiver comprises programmingconfigured to display at least one of a boundary representative of anupper glucose threshold and a boundary representative of a lower glucosethreshold on the user interface.

In an embodiment of the first aspect, the receiver comprises programmingconfigured to permit a user to set the upper glucose threshold and thelower glucose threshold.

In an embodiment of the first aspect, the system further comprises analarm configured to provide at least one of a visual signal, an audiblesignal, and a tactile signal when the calibrated signal is below thelower glucose threshold.

In an embodiment of the first aspect, the system further comprises analarm configured to provide at least one of a visual signal, an audiblesignal, and a tactile signal when the calibrated signal is above theupper glucose threshold.

In an embodiment of the first aspect, the receiver comprises programmingconfigured to estimate glucose data for a future time.

In an embodiment of the first aspect, the system further comprises analarm configured to provide at least one of a visual signal, an audiblesignal, and a tactile signal when the estimated glucose data for thefuture time is above a predetermined threshold.

In an embodiment of the first aspect, the system further comprises analarm configured to provide at least one of a visual signal, an audiblesignal, and a tactile signal when the estimated glucose data for thefuture time is below a predetermined threshold.

In an embodiment of the first aspect, the receiver further comprises asingle point glucose measuring device, wherein the single point glucosemeasuring device is built into the receiver, and wherein thesingle-point glucose measuring device is configured to receive abiological sample from the host and to measure a concentration ofglucose in the biological sample, wherein the measured glucoseconcentration in the biological sample is reference data.

In an embodiment of the first aspect, the programming configured tocalibrate the signal is configured to calibrate the signal at least inpart based on the reference data.

In an embodiment of the first aspect, the receiver comprises programmingconfigured to confirm at least one of the signal and the calibratedsignal at least in part based on the reference data.

In a second aspect, a device is provided comprising a computer readablememory, the computer readable memory comprising code for processing asignal from a continuous glucose measuring device, wherein the codecomprises instructions configured to process a signal received from thecontinuous glucose measuring device; instructions configured tocalibrate the signal; instructions configured to calculate a rate ofchange of the calibrated signal; and instructions configured to displaya graphical representation of the calibrated signal and a directionalarrow indicative of a direction and a rate of change of the calibratedsignal on a user interface.

In an embodiment of the second aspect, the device further comprisesinstructions configured to display at least one of a boundaryrepresentative of an upper glucose threshold and a boundaryrepresentative of a lower glucose threshold on the user interface.

In an embodiment of the second aspect, the device further comprisesinstructions configured allow at least one of the upper glucosethreshold and the lower glucose threshold to be modified by a user.

In an embodiment of the second aspect, the device further comprisesinstructions configured to provide an alarm comprising at least one of avisual signal, an audible signal, and a tactile signal when thecalibrated signal is below the lower glucose threshold.

In an embodiment of the second aspect, the device further comprisesinstructions configured to provide an alarm comprising at least one of avisual signal, an audible signal, and a tactile signal when thecalibrated signal is above the upper glucose threshold.

In an embodiment of the second aspect, the device further comprisesinstructions configured to estimate glucose data for a future time.

In an embodiment of the second aspect, the device further comprisesinstructions configured to provide an alarm comprising at least one of avisual signal, an audible signal, and a tactile signal when theestimated glucose data for the future time is above a predeterminedthreshold.

In an embodiment of the second aspect, the device further comprisesinstructions configured to provide an alarm comprising at least one of avisual signal, an audible signal, and a tactile signal when theestimated glucose data for the future time is below a predeterminedthreshold.

In a third aspect, a method is provided for displaying data from acontinuous glucose measuring device, the method comprising generating asignal from a continuous glucose measuring device indicative of aglucose concentration in a host; calibrating the signal; and displaying,substantially in real-time, a graphical representation of the calibratedsignal and a directional arrow indicative of a direction and a rate ofchange of the calibrated signal.

In an embodiment of the third aspect, the method further comprisesalarming the host when the calibrated signal is below a predeterminedthreshold, wherein the alarm comprises at least one of a visual signal,an audible signal, and a tactile signal.

In an embodiment of the third aspect, the method further comprisesalarming the host when the calibrated signal is above a predeterminedthreshold, wherein the alarm comprises at least one of a visual signal,an audible signal, and a tactile signal.

In an embodiment of the third aspect, the method further comprisesestimating glucose data for a future time.

In an embodiment of the third aspect, the method further comprisesalarming the host when the estimated glucose data for the future time isabove a predetermined threshold, wherein the alarm comprises at leastone of a visual signal, an audible signal, and a tactile signal.

In an embodiment of the third aspect, the method further comprisesalarming the host when the estimated glucose data for the future time isbelow a predetermined threshold, wherein the alarm comprises at leastone of a visual signal, an audible signal, and a tactile signal.

In a fourth aspect, a system is provided for monitoring glucoseconcentration in a host, the system comprising a continuous glucosesensor configured to produce a signal indicative of a glucoseconcentration in a host; and a receiver comprising an alarm, wherein thereceiver is operably connected to the sensor, wherein the receiverfurther comprises programming configured to estimate glucose data for afuture time, and wherein the receiver comprises programming furtherconfigured to trigger the alarm when the estimated glucose data for thefuture time is above or below at least one predetermined threshold.

In an embodiment of the fourth aspect, the alarm is at least one of avisual alarm, an audible alarm, and a tactile alarm.

In an embodiment of the fourth aspect, the predetermined threshold isuser configurable.

In an embodiment of the fourth aspect, the future time is at least about5 minutes in the future.

In an embodiment of the fourth aspect, the future time is at least about10 minutes in the future.

In an embodiment of the fourth aspect, the future time is at least about15 minutes in the future.

In an embodiment of the fourth aspect, the future time is at least about20 minutes in the future.

In an embodiment of the fourth aspect, the receiver comprisesprogramming further configured to calibrate the signal using aconversion function, and wherein the programming configured to estimateglucose data for a future time is configured use the conversion functionto extrapolate glucose data for the future time.

In an embodiment of the fourth aspect, the conversion function iscalculated from a linear regression.

In an embodiment of the fourth aspect, the conversion function iscalculated from a non-linear regression.

In an embodiment of the fourth aspect, the conversion function iscalculated from reference data obtained from a single point glucosemeasuring device.

In an embodiment of the fourth aspect, the single point glucosemeasuring device is built into the receiver.

In an embodiment of the fourth aspect, the receiver comprisesprogramming configured to filter the signal, and wherein the estimatedglucose data is calculated from the filtered signal.

In an embodiment of the fourth aspect, the receiver comprisesprogramming configured to apply at least one boundary to the estimatedglucose data for the future time.

In an embodiment of the fourth aspect, the boundary is a physiologicalboundary.

In an embodiment of the fourth aspect, the receiver further comprises auser interface, wherein the receiver comprises programming furtherconfigured to calibrate the signal, and wherein the receiver comprisesprogramming configured to display a graphical representation of thecalibrated signal and a directional arrow indicative of a direction anda rate of change of the calibrated signal on the user interface.

In a fifth aspect, a device is provided comprising a computer readablememory, the computer readable memory comprising code for processing datafrom a continuous glucose measuring device, wherein the code comprisesinstructions configured to process a signal received from a continuousglucose measuring device; instructions configured to estimate glucosedata for a future time; and instructions configured to trigger an alarmwhen the estimated glucose data for the future time is above or below atleast one predetermined threshold.

In an embodiment of the fifth aspect, the device further comprisesinstructions configured to allow a user to modify the predeterminedthreshold.

In an embodiment of the fifth aspect, the future time is at least about5 minutes in the future.

In an embodiment of the fifth aspect, the future time is at least about15 minutes in the future.

In an embodiment of the fifth aspect, the device further comprisesinstructions configured to calibrate the signal using a conversionfunction, and wherein the instructions configured to estimate glucosedata for a future time are configured use the conversion function toextrapolate glucose data for the future time.

In an embodiment of the fifth aspect, the conversion function iscalculated from a linear regression.

In an embodiment of the fifth aspect, the conversion function iscalculated from a non-linear regression.

In an embodiment of the fifth aspect, the conversion function iscalculated from reference data obtained from a single point glucosemeasuring device.

In an embodiment of the fifth aspect, the single point glucose measuringdevice is integral with the device.

In an embodiment of the fifth aspect, the device further comprisesinstructions configured to filter the signal, and wherein theinstructions configured to estimate glucose data estimate glucose datafrom the filtered signal.

In an embodiment of the fifth aspect, the device further comprisesinstructions configured to apply at least one boundary to the estimatedglucose data for the future time.

In an embodiment of the fifth aspect, the boundary is a physiologicalboundary.

In an embodiment of the fifth aspect, the device further comprisesinstructions configured to calibrate the signal and instructionsconfigured to display a graphical representation of the calibratedsignal and a directional arrow indicative of a direction and a rate ofchange of the calibrated signal on a user interface.

In a sixth aspect, a method is provided for monitoring a glucoseconcentration in a host, the method comprising generating a signal froma continuous glucose measuring device indicative of a glucoseconcentration in a host; processing the signal to estimate glucose datafor a future time; and alarming the host when the estimated glucose datafor the future time is above or below at least one predeterminedthreshold.

In an embodiment of the sixth aspect, the step of alarming comprisesproviding at least one of a visual signal, an audible signal, and atactile signal.

In an embodiment of the sixth aspect, the method further comprisescalibrating the signal using a conversion function, wherein the step ofprocessing the signal to estimate glucose data for a future time isconfigured use the conversion function to extrapolate glucose data forthe future time.

In an embodiment of the sixth aspect, the method further comprises astep of filtering the signal, wherein the step of processing the signalto estimate glucose data estimates the glucose data from the filteredsignal.

In an embodiment of the sixth aspect, the method further comprisesapplying a boundary to the estimated glucose data for the future time.

In an embodiment of the sixth aspect, the method further comprisescalibrating the signal and displaying a graphical representation of thecalibrated signal and a directional arrow indicative of a direction anda rate of change of the calibrated signal on the user interface.

In a seventh aspect, a system is provided for monitoring a glucoseconcentration in a host, the system comprising a continuous glucosesensor configured to produce a signal indicative of a glucoseconcentration in a host; and a receiver operably connected to thesensor, wherein the receiver comprises a single point glucose measuringdevice, wherein the single point glucose measuring device is built intothe receiver, and wherein the single point glucose measuring device isconfigured to receive a biological sample from the host and to measure aconcentration of glucose in the biological sample, wherein the measuredglucose concentration in the biological sample comprises reference data,and wherein the receiver further comprises programming configured tocalibrate or confirm the signal based at least in part on the referencedata.

In an embodiment of the seventh aspect, the receiver comprisesprogramming configured to calibrate and confirm the signal based atleast in part on the reference data.

In an embodiment of the seventh aspect, the receiver comprisesprogramming configured to calibrate the signal only when a rate ofchange of the signal is less than a predetermined threshold.

In an embodiment of the seventh aspect, the signal is a calibratedsignal and wherein the predetermined threshold is 2 mg/dL/min.

In an embodiment of the seventh aspect, the receiver comprisesprogramming configured to evaluate an accuracy of the reference data ascompared to time-corresponding signal data.

In an embodiment of the seventh aspect, the receiver comprisesprogramming configured to prompt the host to provide a biological sampleto the single point glucose measuring device.

In an embodiment of the seventh aspect, the programming configured toprompt the host is based at least in part on events.

In an embodiment of the seventh aspect, the programming configured toprompt the host is based at least in part on timing.

In an embodiment of the seventh aspect, the receiver comprisesprogramming configured to calibrate the signal, and wherein theprogramming is configured to prompt the host based at least in part avalue of the calibrated signal or at least in part rate of change of thecalibrated signal.

In an embodiment of the seventh aspect, the programming configured tocalibrate or confirm is configured to calibrate the signal, wherein thereceiver further comprises a user interface, and wherein the receivercomprises programming configured to display at least one of thecalibrated signal and the reference data on the user interface.

In an embodiment of the seventh aspect, the receiver comprisesprogramming configured to display the calibrated signal and thereference data on the user interface.

In an embodiment of the seventh aspect, the receiver comprises an alarm,wherein the receiver comprises programming configured to estimateglucose data for a future time, and wherein the receiver comprisesprogramming further configured to trigger the alarm when the estimatedglucose data for the future time is above or below at least onepredetermined threshold.

In an embodiment of the seventh aspect, the receiver comprises a userinterface, and wherein the programming configured to calibrate orconfirm is configured to calibrate the signal, to display a graphicalrepresentation of the calibrated signal on the user interface, and todisplay a directional arrow indicative of a direction and a rate ofchange of the calibrated signal on the user interface.

In an eighth aspect, a device is provided comprising a computer readablememory, the computer readable memory comprising code for processing datafrom a continuous glucose measuring device and a single point glucosemeasuring device, wherein the code comprises instructions configured toprocess a signal received from a continuous glucose measuring device;instructions configured to measure a concentration of glucose in abiological sample received from a host, the measured glucoseconcentration in the sample comprising reference data; and instructionsconfigured to calibrate or confirm the signal based at least in part onthe reference data.

In an embodiment of the eighth aspect, the instructions configured tocalibrate or confirm the glucose data are configured to calibrate andconfirm the signal based at least in part on the reference data.

In an embodiment of the eighth aspect, the instructions configured tocalibrate or confirm are configured to calibrate the signal only when arate of change of the signal is less than a predetermined threshold.

In an embodiment of the eighth aspect, the signal is a calibrated signaland wherein the predetermined threshold is 2 mg/dL/min.

In an embodiment of the eighth aspect, the device further comprisesinstructions configured to evaluate an accuracy of the reference data ascompared to time-corresponding signal data.

In an embodiment of the eighth aspect, the device further comprisesinstructions configured to prompt the host to provide a biologicalsample to the single point glucose measuring device.

In an embodiment of the eighth aspect, the instructions configured toprompt the host are based at least in part on events.

In an embodiment of the eighth aspect, the instructions configured toprompt the host are based at least in part on timing.

In an embodiment of the eighth aspect, the instructions configured tocalibrate or confirm are configured to calibrate the signal, and whereinthe instructions configured to prompt the host are based at least inpart on a value of the calibrated signal or at least in part on a rateof change of the calibrated signal.

In an embodiment of the eighth aspect, the instructions configured tocalibrate or confirm are configured to calibrate the signal, and whereinthe device further comprises instructions configured to display at leastone of the calibrated signal and the reference data on a user interface.

In an embodiment of the eighth aspect, the instructions configured todisplay at least one of the calibrated signal and the reference data onthe user interface are configured to display the calibrated signal andthe reference data on the user interface.

In an embodiment of the eighth aspect, the device further comprisesinstructions configured to estimate glucose data for a future time andinstructions configured to trigger an alarm when the estimated glucosedata for the future time is above or below at least one predeterminedthreshold.

In an embodiment of the eighth aspect, the instructions configured tocalibrate or confirm are configured to calibrate the signal, wherein thedevice further comprises instructions configured to display a graphicalrepresentation of the calibrated signal on a user interface andinstructions configured to display a directional arrow indicative of adirection and a rate of change of the calibrated signal on the userinterface.

In a ninth aspect, a method for monitoring glucose concentration in ahost is provided, the method comprising generating a signal from acontinuous glucose measuring device indicative of a glucoseconcentration in a host; receiving the signal from the continuousglucose measuring device in a receiver; measuring a concentration ofglucose in a biological sample in a single point glucose measuringdevice built into the receiver, the measured glucose concentration inthe biological sample comprising reference data; and calibrating orconfirming the signal based at least in part on the reference data.

In an embodiment of the ninth aspect, the step of calibrating orconfirming the signal comprises calibrating and confirming the signalbased at least in part on the reference data.

In an embodiment of the ninth aspect, the step of calibrating the signalis allowed only when a rate of change of the signal is less than apredetermined threshold.

In an embodiment of the ninth aspect, the step of calibrating orconfirming the signal comprises calibrating the signal and wherein thepredetermined threshold is 2 mg/dL/min.

In an embodiment of the ninth aspect, the method further comprisesevaluating an accuracy of the reference data as compared totime-corresponding signal data.

In an embodiment of the ninth aspect, the method further comprisesprompting the host through a user interface to provide a biologicalsample to the single point glucose measuring device.

In an embodiment of the ninth aspect, the step of calibrating orconfirming the signal comprises calibrating the signal, and wherein themethod further comprises displaying at least one of the calibratedsignal and the reference data on a user interface.

In an embodiment of the ninth aspect, the step of displaying at leastone of the calibrated signal and the reference data on a user interfacecomprises displaying the calibrated signal and the reference data on theuser interface.

In an embodiment of the ninth aspect, the method further comprisesestimating glucose data for a future time and triggering an alarm whenthe estimated glucose data for the future time is above or below atleast one predetermined threshold.

In an embodiment of the ninth aspect, the step of calibrating orconfirming the signal comprises calibrating the signal, and wherein themethod further comprises displaying a graphical representation of thecalibrated signal and a directional arrow indicative of a direction anda rate of change of the calibrated signal on a user interface.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of a transcutaneous analyte sensor system,including an applicator, a mounting unit, and an electronics unit.

FIG. 2 is a perspective view of a mounting unit, including theelectronics unit in its functional position.

FIG. 3 is an exploded perspective view of a mounting unit, showing itsindividual components.

FIG. 4A is an exploded perspective view of a contact subassembly,showing its individual components.

FIG. 4B is a perspective view of an alternative contact configuration.

FIG. 4C is a perspective view of another alternative contactconfiguration.

FIG. 5A is an expanded cutaway view of a proximal portion of a sensor.

FIG. 5B is an expanded cutaway view of a distal portion of a sensor.

FIG. 5C is a cross-sectional view through the sensor of FIG. 5B on lineC-C, showing an exposed electroactive surface of a working electrodesurrounded by a membrane system.

FIG. 6 is an exploded side view of an applicator, showing the componentsthat facilitate sensor insertion and subsequent needle retraction.

FIGS. 7A to 7D are schematic side cross-sectional views that illustrateapplicator components and their cooperating relationships.

FIG. 8A is a perspective view of an applicator and mounting unit in oneembodiment including a safety latch mechanism.

FIG. 8B is a side view of an applicator matingly engaged to a mountingunit in one embodiment, prior to sensor insertion.

FIG. 8C is a side view of a mounting unit and applicator depicted in theembodiment of FIG. 8B, after the plunger subassembly has been pushed,extending the needle and sensor from the mounting unit.

FIG. 8D is a side view of a mounting unit and applicator depicted in theembodiment of FIG. 8B, after the guide tube subassembly has beenretracted, retracting the needle back into the applicator.

FIG. 8E is a perspective view of an applicator, in an alternativeembodiment, matingly engaged to the mounting unit after to sensorinsertion.

FIG. 8F is a perspective view of the mounting unit and applicator, asdepicted in the alternative embodiment of FIG. 8E, matingly engagedwhile the electronics unit is slidingly inserted into the mounting unit.

FIG. 8G is a perspective view of the electronics unit, as depicted inthe alternative embodiment of FIG. 8E, matingly engaged to the mountingunit after the applicator has been released.

FIGS. 8H and 8I are comparative top views of the sensor system shown inthe alternative embodiment illustrated in FIGS. 8E to 8G as compared tothe embodiments illustrated in FIGS. 8B to 8D.

FIGS. 9A to 9C are side views of an applicator and mounting unit,showing stages of sensor insertion.

FIGS. 10A and 10B are perspective and side cross-sectional views,respectively, of a sensor system showing the mounting unit immediatelyfollowing sensor insertion and release of the applicator from themounting unit.

FIGS. 11A and 11B are perspective and side cross-sectional views,respectively, of a sensor system showing the mounting unit afterpivoting the contact subassembly to its functional position.

FIGS. 12A to 12C are perspective and side views, respectively, of thesensor system showing the sensor, mounting unit, and electronics unit intheir functional positions.

FIG. 13 is an exploded perspective view of one exemplary embodiment of acontinuous glucose sensor

FIG. 14 is a block diagram that illustrates electronics associated witha sensor system.

FIG. 15 is a perspective view of a sensor system wirelesslycommunicating with a receiver.

FIG. 16A illustrates a first embodiment wherein the receiver shows anumeric representation of the estimated analyte value on its userinterface, which is described in more detail elsewhere herein.

FIG. 16B illustrates a second embodiment wherein the receiver shows anestimated glucose value and one hour of historical trend data on itsuser interface, which is described in more detail elsewhere herein.

FIG. 16C illustrates a third embodiment wherein the receiver shows anestimated glucose value and three hours of historical trend data on itsuser interface, which is described in more detail elsewhere herein.

FIG. 16D illustrates a fourth embodiment wherein the receiver shows anestimated glucose value and nine hours of historical trend data on itsuser interface, which is described in more detail elsewhere herein.

FIG. 17A is a block diagram that illustrates a configuration of amedical device including a continuous analyte sensor, a receiver, and anexternal device.

FIGS. 17B to 17D are illustrations of receiver liquid crystal displaysshowing embodiments of screen displays.

FIG. 18A is a flow chart that illustrates the initial calibration anddata output of sensor data.

FIG. 18B is a perspective view of an integrated receiver housing inanother embodiment, showing a single point glucose monitor including astylus movably mounted to the integrated receiver, wherein the stylus isshown in a storage position.

FIG. 18C is a perspective view of the integrated housing of FIG. 18B,showing the stylus in a testing position.

FIG. 18D is a perspective view of a portion of the stylus of FIG. 18B,showing the sensing region.

FIG. 18E is a perspective view of the integrated receiver housing ofFIG. 18B, showing the stylus loaded with a disposable film, and in itstesting position.

FIG. 18F is a perspective view of a portion of the stylus of FIG. 18B,showing the sensing region with a disposable film stretched and/ordisposed thereon for receiving a biological sample.

FIG. 18G is a graph that illustrates one example of using priorinformation for slope and baseline.

FIG. 19A is a flow chart that illustrates evaluation of reference and/orsensor data for statistical, clinical, and/or physiologicalacceptability.

FIG. 19B is a graph of two data pairs on a Clarke Error Grid toillustrate the evaluation of clinical acceptability in one exemplaryembodiment.

FIG. 20 is a flow chart that illustrates evaluation of calibrated sensordata for aberrant values.

FIG. 21 is a flow chart that illustrates self-diagnostics of sensordata.

FIGS. 22A and 22B are graphical representations of glucose sensor datain a human obtained over approximately three days.

FIG. 23 is a graphical representation of glucose sensor data in a humanobtained over approximately seven days.

FIG. 24 is a flow chart that illustrates the process of estimation ofanalyte values based on measured analyte values in one embodiment.

FIG. 25 is a graph that illustrates the case where estimation istriggered by an event wherein a patient's blood glucose concentrationpasses above a predetermined threshold.

FIG. 26 is a graph that illustrates a raw data stream and correspondingreference analyte values.

FIG. 27 is a flow chart that illustrates the process of compensating fora time lag associated with a continuous analyte sensor to providereal-time estimated analyte data output in one embodiment.

FIG. 28 is a graph that illustrates the data of FIG. 26, includingreference analyte data and corresponding calibrated sensor analyte andestimated sensor analyte data, showing compensation for time lag usingestimation.

FIG. 29 is a flow chart that illustrates the process of matching datapairs from a continuous analyte sensor and a reference analyte sensor inone embodiment.

FIG. 30 is a flow chart that illustrates the process of dynamic andintelligent estimation algorithm selection in one embodiment.

FIG. 31 is a graph that illustrates one case of dynamic and intelligentestimation applied to a data stream, showing first order estimation,second order estimation, and the measured values for a time period,wherein the second order estimation shows a closer correlation to themeasured data than the first order estimation.

FIG. 32 is a flow chart that illustrates the process of estimatinganalyte values within physiological boundaries in one embodiment.

FIG. 33 is a graph that illustrates one case wherein dynamic andintelligent estimation is applied to a data stream, wherein theestimation performs regression and further applies physiologicalconstraints to the estimated analyte data.

FIG. 34 is a flow chart that illustrates the process of dynamic andintelligent estimation and evaluation of analyte values in oneembodiment.

FIG. 35 is a graph that illustrates a case wherein the selectedestimative algorithm is evaluated in one embodiment, wherein acorrelation is measured to determine a deviation of the measured analytedata with the selected estimative algorithm, if any.

FIG. 36 is a flow chart that illustrates the process of evaluating avariation of estimated future analyte value possibilities in oneembodiment.

FIG. 37 is a graph that illustrates a case wherein a variation ofestimated analyte values is based on physiological parameters.

FIG. 38 is a graph that illustrates a case wherein a variation ofestimated analyte values is based on statistical parameters.

FIG. 39 is a flow chart that illustrates the process of estimating,measuring, and comparing analyte values in one embodiment.

FIG. 40 is a graph that illustrates a case wherein a comparison ofestimated analyte values to time corresponding measured analyte valuesis used to determine correlation of estimated to measured analyte data.

FIG. 41 is an illustration of the receiver in one embodiment showing ananalyte trend graph, including measured analyte values, estimatedanalyte values, and a zone of clinical risk.

FIG. 42 is an illustration of the receiver in one embodiment showing agradient bar, including measured analyte values, estimated analytevalues, and a zone of clinical risk.

FIG. 43 is an illustration of the receiver in one embodiment showing ananalyte trend graph, including measured analyte values and one or moreclinically acceptable target analyte values.

FIG. 44 is an illustration of the receiver of FIG. 43, further includingestimated analyte values on the same screen.

FIG. 45 is an illustration of the receiver of FIG. 44, further includinga variation of estimated analyte values and therapy recommendations onthe same screen to help the user obtain the displayed target analytevalues.

FIG. 46 is an illustration of the receiver in one embodiment, showingmeasured analyte values and a dynamic visual representation of a rangeof estimated analyte values based on a variation analysis.

FIG. 47 is an illustration of the receiver in another embodiment,showing measured analyte values and a visual representation of range ofestimated analyte values based on a variation analysis.

FIG. 48 is an illustration of the receiver in another embodiment,showing a numerical representation of the most recent measured analytevalue, a directional arrow indicating rate of change, and a secondarynumerical value representing a variation of the measured analyte value.

FIG. 49 depicts a conventional display of glucose data (uniform y-axis),9-hour trend graph.

FIG. 50 depicts a utility-driven display of glucose data (non-uniformy-axis), 9-hour trend graph.

FIG. 51 depicts a conventional display of glucose data, 7-day glucosechart.

FIG. 52 depicts a utility-driven display of glucose data, 7-day controlchart, median (interquartile range) of daily glucose.

FIG. 53 is an illustration of a receiver in one embodiment thatinterfaces with a computer.

FIG. 54 is an illustration of a receiver in one embodiment thatinterfaces with a modem.

FIG. 55 is an illustration of a receiver in one embodiment thatinterfaces with an insulin pen.

FIG. 56 is an illustration of a receiver in one embodiment thatinterfaces with an insulin pump.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The following description and examples illustrate some exemplaryembodiments of the disclosed invention in detail. Those of skill in theart will recognize that there are numerous variations and modificationsof this invention that are encompassed by its scope. Accordingly, thedescription of a certain exemplary embodiment should not be deemed tolimit the scope of the present invention.

Definitions

In order to facilitate an understanding of the preferred embodiments, anumber of terms are defined below.

The term “analyte” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a substance or chemicalconstituent in a biological fluid (for example, blood, interstitialfluid, cerebral spinal fluid, lymph fluid or urine) that can beanalyzed. Analytes can include naturally occurring substances,artificial substances, metabolites, and/or reaction products. In someembodiments, the analyte for measurement by the sensing regions,devices, and methods is glucose. However, other analytes arecontemplated as well, including but not limited to acarboxyprothrombin;acylcarnitine; adenine phosphoribosyl transferase; adenosine deaminase;albumin; alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle),histidine/urocanic acid, homocysteine, phenylalanine/tyrosine,tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers;arginase; benzoylecgonine (cocaine); biotimidase; biopterin; c-reactiveprotein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholicacid; chloroquine; cholesterol; cholinesterase; conjugated 1-βhydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MMisoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine;dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcoholdehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Beckermuscular dystrophy, glucose-6-phosphate dehydrogenase, hemoglobin A,hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F,D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1,Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax,sexual differentiation, 21-deoxycortisol); desbutylhalofantrine;dihydropteridine reductase; diptheria/tetanus antitoxin; erythrocytearginase; erythrocyte protoporphyrin; esterase D; fattyacids/acylglycines; free β-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; glucose-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I;17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenyloin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszky's disease virus, dengue virus, Dracunculusmedinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus,Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacterium leprae,Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenzavirus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa,respiratory syncytial virus, rickettsia (scrub typhus), Schistosomamansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins, and hormones naturally occurring in blood or interstitialfluids can also constitute analytes in certain embodiments. The analytecan be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte can be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbituates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body can also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC),homovanillic acid (HVA), 5-hydroxytryptamine (5HT), histamine, and5-hydroxyindoleacetic acid (FHIAA).

The term “host” as used herein is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to mammals, particularly humans.

The term “exit-site” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the area where a medical device(for example, a sensor and/or needle) exits from the host's body.

The phrase “continuous (or continual) analyte sensing” as used herein isa broad term, and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and furthermore refers withoutlimitation to the period in which monitoring of analyte concentration iscontinuously, continually, and or intermittently (regularly orirregularly) performed, for example, about every 5 to 10 minutes.

The term “electrochemically reactive surface” as used herein is a broadterm, and is to be given its ordinary and customary meaning to a personof ordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to thesurface of an electrode where an electrochemical reaction takes place.For example, a working electrode measures hydrogen peroxide produced bythe enzyme-catalyzed reaction of the analyte detected, which reacts tocreate an electric current. Glucose analyte can be detected utilizingglucose oxidase, which produces H₂O₂ as a byproduct. H₂O₂ reacts withthe surface of the working electrode, producing two protons (2H⁺), twoelectrons (2e⁻) and one molecule of oxygen (O₂), which produces theelectronic current being detected.

The term “electronic connection” as used herein is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to any electronicconnection known to those in the art that can be utilized to interfacethe sensing region electrodes with the electronic circuitry of a device,such as mechanical (for example, pin and socket) or soldered electronicconnections.

The term “interferant” and “interferants” as used herein is a broadterm, and is to be given its ordinary and customary meaning to a personof ordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation tospecies that interfere with the measurement of an analyte of interest ina sensor to produce a signal that does not accurately represent theanalyte measurement. In one example of an electrochemical sensor,interferants are compounds with oxidation potentials that overlap withthe analyte to be measured.

The term “sensing region” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to the region of amonitoring device responsible for the detection of a particular analyte.The sensing region generally comprises a non-conductive body, a workingelectrode (anode), a reference electrode (optional), and/or a counterelectrode (cathode) passing through and secured within the body formingelectrochemically reactive surfaces on the body and an electronicconnective means at another location on the body, and a multi-domainmembrane affixed to the body and covering the electrochemically reactivesurface.

The term “high oxygen solubility domain” as used herein is a broad term,and is to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to adomain composed of a material that has higher oxygen solubility thanaqueous media such that it concentrates oxygen from the biological fluidsurrounding the membrane system. The domain can act as an oxygenreservoir during times of minimal oxygen need and has the capacity toprovide, on demand, a higher oxygen gradient to facilitate oxygentransport across the membrane. Thus, the ability of the high oxygensolubility domain to supply a higher flux of oxygen to critical domainswhen needed can improve overall sensor function.

The term “domain” as used herein is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a region of the membrane systemthat can be a layer, a uniform or non-uniform gradient (for example, ananisotropic region of a membrane), or a portion of a membrane.

The phrase “distal to” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to the spatialrelationship between various elements in comparison to a particularpoint of reference. In general, the term indicates an element is locatedrelatively far from the reference point than another element.

The term “proximal to” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to the spatialrelationship between various elements in comparison to a particularpoint of reference. In general, the term indicates an element is locatedrelatively near to the reference point than another element.

The terms “in vivo portion” and “distal portion” as used herein arebroad terms, and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto the portion of the device (for example, a sensor) adapted forinsertion into and/or existence within a living body of a host.

The terms “ex vivo portion” and “proximal portion” as used herein arebroad terms, and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto the portion of the device (for example, a sensor) adapted to remainand/or exist outside of a living body of a host.

The terms “raw data stream” and “data stream” as used herein are broadterms, and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto an analog or digital signal from the analyte sensor directly relatedto the measured analyte. For example, the raw data stream is digitaldata in “counts” converted by an A/D converter from an analog signal(for example, voltage or amps) representative of an analyteconcentration. The terms broadly encompass a plurality of time spaceddata points from a substantially continuous analyte sensor, each ofwhich comprises individual measurements taken at time intervals rangingfrom fractions of a second up to, for example, 1, 2, or 5 minutes orlonger.

The term “count” as used herein is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a unit of measurement of adigital signal. For example, a raw data stream measured in counts isdirectly related to a voltage (for example, converted by an A/Dconverter), which is directly related to current from the workingelectrode.

The term “physiologically feasible” as used herein is a broad term, andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to one ormore physiological parameters obtained from continuous studies ofglucose data in humans and/or animals. For example, a maximal sustainedrate of change of glucose in humans of about 4 to 6 mg/dL/min and amaximum acceleration of the rate of change of about 0.1 to 0.2mg/dL/min/min are deemed physiologically feasible limits. Values outsideof these limits are considered non-physiological and are likely a resultof, e.g. signal error.

The term “ischemia” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to local and temporary deficiencyof blood supply due to obstruction of circulation to a part (forexample, a sensor). Ischemia can be caused, for example, by mechanicalobstruction (for example, arterial narrowing or disruption) of the bloodsupply.

The term “matched data pairs” as used herein is a broad term, and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to reference data(for example, one or more reference analyte data points) matched withsubstantially time corresponding sensor data (for example, one or moresensor data points).

The term “Clarke Error Grid” as used herein is a broad term, and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an error gridanalysis, for example, an error grid analysis used to evaluate theclinical significance of the difference between a reference glucosevalue and a sensor generated glucose value, taking into account 1) thevalue of the reference glucose measurement, 2) the value of the sensorglucose measurement, 3) the relative difference between the two values,and 4) the clinical significance of this difference. See Clarke et al.,“Evaluating Clinical Accuracy of Systems for Self-Monitoring of BloodGlucose” Diabetes Care, Volume 10, Number 5, September-October 1987.

The term “Consensus Error Grid” as used herein is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an error gridanalysis that assigns a specific level of clinical risk to any possibleerror between two time corresponding measurements, e.g. glucosemeasurements. The Consensus Error Grid is divided into zones signifyingthe degree of risk posed by the deviation. See Parkes et al., “A NewConsensus Error Grid to Evaluate the Clinical Significance ofInaccuracies in the Measurement of Blood Glucose” Diabetes Care, Volume23, Number 8, August 2000.

The term “clinical acceptability” as used herein is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to determination ofthe risk of an inaccuracy to a patient. Clinical acceptability considersa deviation between time corresponding analyte measurements (forexample, data from a glucose sensor and data from a reference glucosemonitor) and the risk (for example, to the decision making of a personwith diabetes) associated with that deviation based on the analyte valueindicated by the sensor and/or reference data. An example of clinicalacceptability can be 85% of a given set of measured analyte valueswithin the “A” and “B” region of a standard Clarke Error Grid when thesensor measurements are compared to a standard reference measurement.

The term “sensor” as used herein is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the component or region of adevice by which an analyte can be quantified.

The term “needle” as used herein is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a slender hollow instrument forintroducing material into or removing material from the body.

The terms “operably connected” and “operably linked” as used herein arebroad terms, and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto one or more components linked to one or more other components. Theterms can refer to a mechanical connection, an electrical connection, ora connection that allows transmission of signals between the components,e.g., wired or wirelessly. For example, one or more electrodes can beused to detect the amount of analyte in a sample and to convert thatinformation into a signal; the signal can then be transmitted to acircuit. In such an example, the electrode is “operably linked” to theelectronic circuitry.

The term “baseline” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the component of an analytesensor signal that is not related to the analyte concentration. In oneexample of a glucose sensor, the baseline is composed substantially ofsignal contribution due to factors other than glucose (for example,interfering species, non-reaction-related hydrogen peroxide, or otherelectroactive species with an oxidation potential that overlaps withhydrogen peroxide). In some embodiments wherein a calibration is definedby solving for the equation y=m×+b, the value of b represents thebaseline of the signal.

The terms “sensitivity” and “slope” as used herein are broad terms, andare to be given their ordinary and customary meaning to a person ofordinary skill in the art (and are not to be limited to a special orcustomized meaning), and furthermore refer without limitation to anamount of electrical current produced by a predetermined amount (unit)of the measured analyte. For example, in one preferred embodiment, asensor has a sensitivity (or slope) of about 3.5 to about 7.5 picoAmpsof current for every 1 mg/dL of glucose analyte.

The term “membrane system” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a permeable orsemi-permeable membrane that can be comprised of two or more domains andis typically constructed of materials of a few microns thickness ormore, which is permeable to oxygen and is optionally permeable to, e.g.,glucose or another analyte. In one example, the membrane systemcomprises an immobilized glucose oxidase enzyme, which enables areaction to occur between glucose and oxygen whereby a concentration ofglucose can be measured.

The terms “processor,” “processor module” and “microprocessor” as usedherein are broad terms, and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and are not to belimited to a special or customized meaning), and furthermore referwithout limitation to a computer system, state machine, processor, orthe like designed to perform arithmetic or logic operations using logiccircuitry that responds to and processes the basic instructions thatdrive a computer.

The terms “smoothing” and “filtering” as used herein are broad terms,and are to be given their ordinary and customary meaning to a person ofordinary skill in the art (and are not to be limited to a special orcustomized meaning), and furthermore refer without limitation tomodification of a set of data to make it smoother and more continuous orto remove or diminish outlying points, for example, by performing amoving average of the raw data stream.

The term “algorithm” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a computational process (forexample, programs) involved in transforming information from one stateto another, for example, by using computer processing.

The term “regression” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to finding a line for which a setof data has a minimal measurement (for example, deviation) from thatline. Regression can be linear, non-linear, first order, second order,or the like. One example of regression is least squares regression.

The term “calibration” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to the process ofdetermining the relationship between the sensor data and thecorresponding reference data, which can be used to convert sensor datainto meaningful values substantially equivalent to the reference data.In some embodiments, namely, in continuous analyte sensors, calibrationcan be updated or recalibrated over time as changes in the relationshipbetween the sensor data and reference data occur, for example, due tochanges in sensitivity, baseline, transport, metabolism, or the like.

The terms “chloridization” and “chloridizing” as used herein are broadterms, and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto treatment or preparation with chloride. The term “chloride” as usedherein, is a broad term and is used in its ordinary sense, including,without limitation, to refer to Cl⁻ ions, sources of Cl⁻ ions, and saltsof hydrochloric acid. Chloridization and chloridizing methods include,but are not limited to, chemical and electrochemical methods.

The term “raw data signal” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an analog ordigital signal directly related to the measured analyte from the analytesensor. In one example, the raw data signal is digital data in “counts”converted by an A/D converter from an analog signal (e.g. voltage oramps) representative of an analyte concentration.

The term “counts” as used herein is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a unit of measurement of adigital signal. In one example, a raw data signal measured in counts isdirectly related to a voltage (converted by an A/D converter), which isdirectly related to current.

The term “R-value” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to one conventional way ofsummarizing the correlation of data; that is, a statement of whatresiduals (e.g. root mean square deviations) are to be expected if thedata are fitted to a straight line by the a regression.

The term “data association” and “data association function” as usedherein are broad terms, and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and are not to belimited to a special or customized meaning), and furthermore referwithout limitation to a statistical analysis of data and particularlyits correlation to, or deviation from, from a particular curve. A dataassociation function is used to show data association. For example, thedata that forms that calibration set as described herein can be analyzedmathematically to determine its correlation to, or deviation from, acurve (e.g. line or set of lines) that defines the conversion function;this correlation or deviation is the data association. A dataassociation function is used to determine data association. Examples ofdata association functions include, but are not limited to, linearregression, non-linear mapping/regression, rank (e.g., non-parametric)correlation, least mean square fit, mean absolute deviation (MAD), meanabsolute relative difference. In one such example, the correlationcoefficient of linear regression is indicative of the amount of dataassociation of the calibration set that forms the conversion function,and thus the quality of the calibration.

The term “quality of calibration” as used herein is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to the statisticalassociation of matched data pairs in the calibration set used to createthe conversion function. For example, an R-value can be calculated for acalibration set to determine its statistical data association, whereinan R-value greater than 0.79 determines a statistically acceptablecalibration quality, while an R-value less than 0.79 determinesstatistically unacceptable calibration quality.

The term “substantially” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to being largely butnot necessarily wholly that which is specified.

The term “congruence” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the quality or state ofagreeing, coinciding, or being concordant. In one example, congruencecan be determined using rank correlation.

The term “concordant” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to being in agreement or harmony,and/or free from discord.

The term “estimation algorithm” as used herein is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to the processinginvolved in estimating analyte values from measured analyte values for atime period during which no data exists (e.g., for a future time periodor during data gaps). This term is broad enough to include one or aplurality of algorithms and/or computations. This term is also broadenough to include algorithms or computations based on mathematical,statistical, clinical, and/or physiological information.

The terms “recursive filter” and “auto-regressive algorithm” as usedherein are broad terms, and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and are not to belimited to a special or customized meaning), and furthermore referwithout limitation to an equation in which includes previous averagesare part of the next filtered output. More particularly, the generationof a series of observations whereby the value of each observation ispartly dependent on the values of those that have immediately precededit. One example is a regression structure in which lagged responsevalues assume the role of the independent variables.

The terms “velocity” and “rate of change” as used herein are broadterms, and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto time rate of change; the amount of change divided by the timerequired for the change. In one embodiment, these terms refer to therate of increase or decrease in an analyte for a certain time period.

The term “acceleration” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to the rate ofchange of velocity with respect to time. This term is broad enough toinclude deceleration.

The term “variation” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a divergence or amount ofchange from a point, line, or set of data. In one embodiment, estimatedanalyte values can have a variation including a range of values outsideof the estimated analyte values that represent a range of possibilitiesbased on known physiological patterns, for example.

The term “deviation” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a statistical measurerepresenting the difference between different data sets. The term isbroad enough to encompass the deviation represented as a correlation ofdata.

The terms “statistical parameters” and “statistical” as used herein arebroad terms, and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto information computed from the values of a sampling of data. Forexample, noise or variability in data can be statistically measured.

The term “statistical variation” as used herein is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to divergence orchange from a point, line, or set of data based on statisticalinformation. The term “statistical information” is broad enough toinclude patterns or data analysis resulting from experiments, publishedor unpublished, for example.

The term “clinical risk” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an identifieddanger or potential risk to the health of a patient based on a measuredor estimated analyte concentration, its rate of change, and/or itsacceleration. In one exemplary embodiment, clinical risk is determinedby a measured glucose concentration above or below a threshold (forexample, 80-200 mg/dL) and/or its rate of change.

The term “clinically acceptable” as used herein is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an analyteconcentration, rate of change, and/or acceleration associated with thatmeasured analyte that is considered to be safe for a patient. In oneexemplary embodiment, clinical acceptability is determined by a measuredglucose concentration within a threshold (for example, 80-200 mg/dL)and/or its rate of change.

The terms “physiological parameters” and “physiological boundaries” asused herein are broad terms, and are to be given their ordinary andcustomary meaning to a person of ordinary skill in the art (and are notto be limited to a special or customized meaning), and furthermore referwithout limitation to the parameters obtained from continuous studies ofphysiological data in humans and/or animals. For example, a maximalsustained rate of change of glucose in humans of about 4 to 6 mg/dL/minand a maximum acceleration of the rate of change of about 0.1 to 0.2mg/dL/min² are deemed physiologically feasible limits; values outside ofthese limits would be considered non-physiological. As another example,the rate of change of glucose is lowest at the maxima and minima of thedaily glucose range, which are the areas of greatest risk in patienttreatment, thus a physiologically feasible rate of change can be set atthe maxima and minima based on continuous studies of glucose data. As afurther example, it has been observed that the best solution for theshape of the curve at any point along glucose signal data stream over acertain time period (for example, about 20 to 30 minutes) is a straightline, which can be used to set physiological limits. These terms arebroad enough to include physiological parameters for any analyte.

The terms “individual physiological patterns” and “individual historicalpatterns” as used herein are broad terms, and are to be given theirordinary and customary meaning to a person of ordinary skill in the art(and are not to be limited to a special or customized meaning), andfurthermore refer without limitation to patterns obtained by monitoringa physiological characteristic, such as glucose concentration, in amammal over a time period. For example, continual or continuousmonitoring of glucose concentration in humans can recognize a “normal”pattern of turnaround at the human's lowest glucose levels.

The term “physiological variation” as used herein is a broad term, andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation todivergence or change from a point, line, or set of data based on knownphysiological parameters and/or patterns.

The terms “clinical error grid,” “clinical error analysis” and “errorgrid analysis” as used herein are broad terms, and are to be given theirordinary and customary meaning to a person of ordinary skill in the art(and are not to be limited to a special or customized meaning), andfurthermore refer without limitation to an analysis method that assignsa specific level of clinical risk to an error between two timecorresponding analyte measurements. Examples include Clarke Error Grid,Consensus Grid, mean absolute relative difference, rate grid, or otherclinical cost functions.

The term “Clarke Error Grid” as used herein is a broad term, and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an error gridanalysis, which evaluates the clinical significance of the differencebetween a reference glucose value and a sensor generated glucose value,taking into account 1) the value of the reference glucose measurement,2) the value of the sensor glucose measurement, 3) the relativedifference between the two values, and 4) the clinical significance ofthis difference. See Clarke et al., “Evaluating Clinical Accuracy ofSystems for Self-Monitoring of Blood Glucose,” Diabetes Care, Volume 10,Number 5, September-October 1987, which is incorporated by referenceherein in its entirety.

The term “rate grid” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to an error grid analysis, whichevaluates the clinical significance of the difference between areference glucose value and a continuous sensor generated glucose value,taking into account both single-point and rate-of-change values. Oneexample of a rate grid is described in Kovatchev, B. P.;Gonder-Frederick, L. A.; Cox, D. J.; Clarke, W. L. Evaluating theaccuracy of continuous glucose-monitoring sensors: continuousglucose-error grid analysis illustrated by TheraSense FreestyleNavigator data. Diabetes Care 2004, 27, 1922-1928.

The term “curvature formula” as used herein is a broad term, and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a mathematicalformula that can be used to define a curvature of a line. Some examplesof curvature formulas include Euler and Rodrigues' curvature formulas.

The term “time period” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an amount of timeincluding a single point in time and a path (for example, range of time)that extends from a first point in time to a second point in time.

The term “measured analyte values” as used herein is a broad term, andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to ananalyte value or set of analyte values for a time period for whichanalyte data has been measured by an analyte sensor. The term is broadenough to include data from the analyte sensor before or after dataprocessing in the sensor and/or receiver (for example, data smoothing,calibration, or the like).

The term “estimated analyte values” as used herein is a broad term, andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to ananalyte value or set of analyte values, which have been algorithmicallyextrapolated from measured analyte values. Typically, estimated analytevalues are estimated for a time period during which no data exists.However, estimated analyte values can also be estimated during a timeperiod for which measured data exists, but is to be replaced byalgorithmically extrapolated data due to a time lag in the measureddata, for example.

The term “alarm” as used herein is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to audible, visual, or tactilesignals that are triggered in response to detection of clinical risk toa patient. In one embodiment, hyperglycemic and hypoglycemic alarms aretriggered when present or future clinical danger is assessed based oncontinuous analyte data.

The terms “target analyte values” and “analyte value goal” as usedherein are broad terms, and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and are not to belimited to a special or customized meaning), and furthermore referwithout limitation to an analyte value or set of analyte values that areclinically acceptable. In one example, a target analyte value isvisually or audibly presented to a patient in order to aid in guidingthe patient in understanding how they should avoid a clinically riskyanalyte concentration.

The terms “therapy” and “therapy recommendations” as used herein arebroad terms, and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto the treatment of disease or disorder by any method. In one exemplaryembodiment, a patient is prompted with therapy recommendations such as“inject insulin” or “consume carbohydrates” in order to avoid aclinically risky glucose concentration.

The phrase “continuous glucose sensing,” as used herein, is a broad termand is used in its ordinary sense, including, without limitation, theperiod in which monitoring of plasma glucose concentration iscontinuously or continually performed, for example, at time intervalsranging from fractions of a second up to, for example, 1, 2, or 5minutes, or longer.

The term “single point glucose monitor,” as used herein, is a broad termand is used in its ordinary sense, including, without limitation, adevice that can be used to measure a glucose concentration within a hostat a single point in time, for example, some embodiments utilize a smallvolume in vitro glucose monitor that includes an enzyme membrane such asdescribed with reference to U.S. Pat. Nos. 4,994,167 and 4,757,022. Itshould be understood that single point glucose monitors can measuremultiple samples (for example, blood or interstitial fluid); howeveronly one sample is measured at a time and typically requires some userinitiation and/or interaction.

The term “biological sample,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, sample of ahost body, for example blood, interstitial fluid, spinal fluid, saliva,urine, tears, sweat, or the like.

Sensor System

The preferred embodiments relate to the use of an analyte sensor thatmeasures a concentration of analyte of interest or a substanceindicative of the concentration or presence of the analyte. In someembodiments, the sensor is a continuous device, for example asubcutaneous, transdermal (e.g., transcutaneous), or intravasculardevice. In some embodiments, the device can analyze a plurality ofintermittent blood samples. The analyte sensor can use any method ofanalyte-sensing, including enzymatic, chemical, physical,electrochemical, spectrophotometric, polarimetric, calorimetric,radiometric, or the like.

The analyte sensor uses any method, including invasive, minimallyinvasive, and non-invasive sensing techniques, to provide an outputsignal indicative of the concentration of the analyte of interest. Theoutput signal is typically a raw signal that is used to provide a usefulvalue of the analyte of interest to a user, such as a patient orphysician, who can be using the device. Accordingly, appropriatesmoothing, calibration, and evaluation methods can be applied to the rawsignal and/or system as a whole to provide relevant and acceptableestimated analyte data to the user.

The methods and devices of preferred embodiments can be employed in acontinuous glucose sensor that measures a concentration of glucose or asubstance indicative of a concentration or presence of the glucose.However, certain methods and devices of preferred embodiments are alsosuitable for use in connection with non-continuous (e.g., single pointmeasurement or finger stick) monitors, such as the OneTouch® systemmanufactured by Lifescan, Inc., or monitors as disclosed in U.S. Pat.Nos. 5,418,142; 5,515,170; 5,526,120; 5,922,530; 5,968,836; and6,335,203. In some embodiments, the glucose sensor is an invasive,minimally-invasive, or non-invasive device, for example a subcutaneous,transdermal, or intravascular device. In some embodiments, the devicecan analyze a plurality of intermittent biological samples, such asblood, interstitial fluid, or the like. The glucose sensor can use anymethod of glucose-measurement, including calorimetric, enzymatic,chemical, physical, electrochemical, spectrophotometric, polarimetric,calorimetric, radiometric, or the like. In alternative embodiments, thesensor can be any sensor capable of determining the level of an analytein the body, for example oxygen, lactase, hormones, cholesterol,medicaments, viruses, or the like.

The glucose sensor uses any suitable method to provide an output signalindicative of the concentration of the glucose. The output signal istypically a raw data stream that is used to provide a value indicativeof the measured glucose concentration to a patient or doctor, forexample.

One exemplary embodiment described in detail below utilizes animplantable glucose sensor. Another exemplary embodiment described indetail below utilizes a transcutaneous glucose sensor.

In one alternative embodiment, the continuous glucose sensor comprises atranscutaneous sensor such as described in U.S. Pat. No. 6,565,509 toSay et al. In another alternative embodiment, the continuous glucosesensor comprises a subcutaneous sensor such as described with referenceto U.S. Pat. No. 6,579,690 to Bonnecaze et al. or U.S. Pat. No.6,484,046 to Say et al. In another alternative embodiment, thecontinuous glucose sensor comprises a refillable subcutaneous sensorsuch as described with reference to U.S. Pat. No. 6,512,939 to Colvin etal. In another alternative embodiment, the continuous glucose sensorcomprises an intravascular sensor such as described with reference toU.S. Pat. No. 6,477,395 to Schulman et al. In another alternativeembodiment, the continuous glucose sensor comprises an intravascularsensor such as described with reference to U.S. Pat. No. 6,424,847 toMastrototaro et al. All of the above patents are incorporated in theirentirety herein by reference.

Although a few exemplary embodiments of continuous glucose sensors areillustrated and described herein, it should be understood that thedisclosed embodiments are applicable to any device capable of singleanalyte, substantially continual or substantially continuous measurementof a concentration of analyte of interest and providing an output signalthat represents the concentration of that analyte.

In a first exemplary embodiment, a transcutaneous analyte sensor systemis provided that includes an applicator for inserting the transdermalanalyte sensor under a host's skin. The sensor system includes a sensorfor sensing the analyte, wherein the sensor is associated with amounting unit adapted for mounting on the skin of the host. The mountingunit houses the electronics unit associated with the sensor and isadapted for fastening to the host's skin. In certain embodiments, thesystem further includes a receiver for receiving and/or processingsensor data.

FIG. 1 is a perspective view of a transcutaneous analyte sensor system10. In the preferred embodiment of a system as depicted in FIG. 1, thesensor includes an applicator 12, a mounting unit 14, and an electronicsunit 16. The system can further include a receiver 158, such as isdescribed in more detail with reference to FIG. 15.

The mounting unit (housing) 14 includes a base 24 adapted for mountingon the skin of a host, a sensor adapted for transdermal insertionthrough the skin of a host (see FIG. 4A), and one or more contacts 28configured to provide secure electrical contact between the sensor andthe electronics unit 16. The mounting unit 14 is designed to maintainthe integrity of the sensor in the host so as to reduce or eliminatetranslation of motion between the mounting unit, the host, and/or thesensor.

In one embodiment, an applicator 12 is provided for inserting the sensor32 through the host's skin at the appropriate insertion angle with theaid of a needle (see FIGS. 6 through 8), and for subsequent removal ofthe needle using a continuous push-pull action. Preferably, theapplicator comprises an applicator body 18 that guides the applicatorcomponents (see FIGS. 6 through 8) and includes an applicator body base60 configured to mate with the mounting unit 14 during insertion of thesensor into the host. The mate between the applicator body base 60 andthe mounting unit 14 can use any known mating configuration, forexample, a snap-fit, a press-fit, an interference-fit, or the like, todiscourage separation during use. One or more release latches 30 enablerelease of the applicator body base 60, for example, when the applicatorbody base 60 is snap fit into the mounting unit 14.

The electronics unit 16 includes hardware, firmware, and/or softwarethat enable measurement of levels of the analyte via the sensor. Forexample, the electronics unit 16 can comprise a potentiostat, a powersource for providing power to the sensor, other components useful forsignal processing, and preferably an RF module for transmitting datafrom the electronics unit 16 to a receiver (see FIGS. 14 to 16).Electronics can be affixed to a printed circuit board (PCB), or thelike, and can take a variety of forms. For example, the electronics cantake the form of an integrated circuit (IC), such as anApplication-Specific Integrated Circuit (ASIC), a microcontroller, or aprocessor. Preferably, electronics unit 16 houses the sensorelectronics, which comprise systems and methods for processing sensoranalyte data. Examples of systems and methods for processing sensoranalyte data are described in more detail below and in U.S. PatentPublication No. US-2005-0027463-A1.

After insertion of the sensor using the applicator 12, and subsequentrelease of the applicator 12 from the mounting unit 14 (see FIGS. 8B to8D), the electronics unit 16 is configured to releasably mate with themounting unit 14 in a manner similar to that described above withreference to the applicator body base 60. The electronics unit 16includes contacts on its backside (not shown) configured to electricallyconnect with the contacts 28, such as are described in more detail withreference to FIGS. 2 through 4. In one embodiment, the electronics unit16 is configured with programming, for example initialization,calibration reset, failure testing, or the like, each time it isinitially inserted into the mounting unit 14 and/or each time itinitially communicates with the sensor 32.

Mounting Unit

FIG. 2 is a perspective view of a sensor system of a preferredembodiment, shown in its functional position, including a mounting unitand an electronics unit matingly engaged therein. FIGS. 8 to 10illustrate the sensor is its functional position for measurement of ananalyte concentration in a host.

In preferred embodiments, the mounting unit 14, also referred to as ahousing, comprises a base 24 adapted for fastening to a host's skin. Thebase can be formed from a variety of hard or soft materials, andpreferably comprises a low profile for minimizing protrusion of thedevice from the host during use. In some embodiments, the base 24 isformed at least partially from a flexible material, which is believed toprovide numerous advantages over conventional transcutaneous sensors,which, unfortunately, can suffer from motion-related artifactsassociated with the host's movement when the host is using the device.For example, when a transcutaneous analyte sensor is inserted into thehost, various movements of the sensor (for example, relative movementbetween the in vivo portion and the ex vivo portion, movement of theskin, and/or movement within the host (dermis or subcutaneous)) createstresses on the device and can produce noise in the sensor signal. It isbelieved that even small movements of the skin can translate todiscomfort and/or motion-related artifact, which can be reduced orobviated by a flexible or articulated base. Thus, by providingflexibility and/or articulation of the device against the host's skin,better conformity of the sensor system 10 to the regular use andmovements of the host can be achieved. Flexibility or articulation isbelieved to increase adhesion (with the use of an adhesive pad) of themounting unit 14 onto the skin, thereby decreasing motion-relatedartifact that can otherwise translate from the host's movements andreduced sensor performance.

FIG. 3 is an exploded perspective view of a sensor system of a preferredembodiment, showing a mounting unit, an associated contact subassembly,and an electronics unit. In some embodiments, the contacts 28 aremounted on or in a subassembly hereinafter referred to as a contactsubassembly 26 (see FIG. 4A), which includes a contact holder 34configured to fit within the base 24 of the mounting unit 14 and a hinge38 that allows the contact subassembly 26 to pivot between a firstposition (for insertion) and a second position (for use) relative to themounting unit 14, which is described in more detail with reference toFIGS. 10 and 11. The term “hinge” as used herein is a broad term and isused in its ordinary sense, including, without limitation, to refer toany of a variety of pivoting, articulating, and/or hinging mechanisms,such as an adhesive hinge, a sliding joint, and the like; the term hingedoes not necessarily imply a fulcrum or fixed point about which thearticulation occurs.

In certain embodiments, the mounting unit 14 is provided with anadhesive pad 8, preferably disposed on the mounting unit's back surfaceand preferably including a releasable backing layer 9. Thus, removingthe backing layer 9 and pressing the base portion 24 of the mountingunit onto the host's skin adheres the mounting unit 14 to the host'sskin. Additionally or alternatively, an adhesive pad can be placed oversome or all of the sensor system after sensor insertion is complete toensure adhesion, and optionally to ensure an airtight seal or watertightseal around the wound exit-site (or sensor insertion site) (not shown).Appropriate adhesive pads can be chosen and designed to stretch,elongate, conform to, and/or aerate the region (e.g., host's skin).

In preferred embodiments, the adhesive pad 8 is formed from spun-laced,open- or closed-cell foam, and/or non-woven fibers, and includes anadhesive disposed thereon, however a variety of adhesive padsappropriate for adhesion to the host's skin can be used, as isappreciated by one skilled in the art of medical adhesive pads. In someembodiments, a double-sided adhesive pad is used to adhere the mountingunit to the host's skin. In other embodiments, the adhesive pad includesa foam layer, for example, a layer wherein the foam is disposed betweenthe adhesive pad's side edges and acts as a shock absorber.

In some embodiments, the surface area of the adhesive pad 8 is greaterthan the surface area of the mounting unit's back surface.Alternatively, the adhesive pad can be sized with substantially the samesurface area as the back surface of the base portion. Preferably, theadhesive pad has a surface area on the side to be mounted on the host'sskin that is greater than about 1, 1.25, 1.5, 1.75, 2, 2.25, or 2.5,times the surface area of the back surface 25 of the mounting unit base24. Such a greater surface area can increase adhesion between themounting unit and the host's skin, minimize movement between themounting unit and the host's skin, and/or protect the wound exit-site(sensor insertion site) from environmental and/or biologicalcontamination. In some alternative embodiments, however, the adhesivepad can be smaller in surface area than the back surface assuming asufficient adhesion can be accomplished.

In some embodiments, the adhesive pad 8 is substantially the same shapeas the back surface 25 of the base 24, although other shapes can also beadvantageously employed, for example, butterfly-shaped, round, square,or rectangular. The adhesive pad backing can be designed for two-steprelease, for example, a primary release wherein only a portion of theadhesive pad is initially exposed to allow adjustable positioning of thedevice, and a secondary release wherein the remaining adhesive pad islater exposed to firmly and securely adhere the device to the host'sskin once appropriately positioned. The adhesive pad is preferablywaterproof. Preferably, a stretch-release adhesive pad is provided onthe back surface of the base portion to enable easy release from thehost's skin at the end of the useable life of the sensor, as isdescribed in more detail with reference to FIGS. 9A to 9C.

In some circumstances, it has been found that a conventional bondbetween the adhesive pad and the mounting unit may not be sufficient,for example, due to humidity that can cause release of the adhesive padfrom the mounting unit. Accordingly, in some embodiments, the adhesivepad can be bonded using a bonding agent activated by or accelerated byan ultraviolet, acoustic, radio frequency, or humidity cure. In someembodiments, a eutectic bond of first and second composite materials canform a strong adhesion. In some embodiments, the surface of the mountingunit can be pretreated utilizing ozone, plasma, chemicals, or the like,in order to enhance the bondability of the surface.

A bioactive agent is preferably applied locally at the insertion site(exit-site) prior to or during sensor insertion. Suitable bioactiveagents include those which are known to discourage or prevent bacterialgrowth and infection, for example, anti-inflammatory agents,antimicrobials, antibiotics, or the like. It is believed that thediffusion or presence of a bioactive agent can aid in prevention orelimination of bacteria adjacent to the exit-site. Additionally oralternatively, the bioactive agent can be integral with or coated on theadhesive pad, or no bioactive agent at all is employed.

FIG. 4A is an exploded perspective view of the contact subassembly 26 inone embodiment, showing its individual components. Preferably, awatertight (waterproof or water-resistant) sealing member 36, alsoreferred to as a sealing material, fits within a contact holder 34 andprovides a watertight seal configured to surround the electricalconnection at the electrode terminals within the mounting unit in orderto protect the electrodes (and the respective operable connection withthe contacts of the electronics unit 16) from damage due to moisture,humidity, dirt, and other external environmental factors. In oneembodiment, the sealing member 36 is formed from an elastomericmaterial, such as silicone; however, a variety of other elastomeric orsealing materials can also be used. In alternative embodiments, the sealis designed to form an interference fit with the electronics unit andcan be formed from a variety of materials, for example, flexibleplastics or noble metals. One of ordinary skill in the art appreciatesthat a variety of designs can be employed to provide a seal surroundingthe electrical contacts described herein. For example, the contactholder 34 can be integrally designed as a part of the mounting unit,rather than as a separate piece thereof. Additionally or alternatively,a sealant can be provided in or around the sensor (e.g. within or on thecontact subassembly or sealing member), such as is described in moredetail with reference to FIGS. 11A and 11B.

In the illustrated embodiment, the sealing member 36 is formed with araised portion 37 surrounding the contacts 28. The raised portion 37enhances the interference fit surrounding the contacts 28 when theelectronics unit 16 is mated to the mounting unit 14. Namely, the raisedportion surrounds each contact and presses against the electronics unit16 to form a tight seal around the electronics unit.

Contacts 28 fit within the seal 36 and provide for electrical connectionbetween the sensor 32 and the electronics unit 16. In general, thecontacts are designed to ensure a stable mechanical and electricalconnection of the electrodes that form the sensor 32 (see FIG. 5A to 5C)to mutually engaging contacts 28 thereon. A stable connection can beprovided using a variety of known methods, for example, domed metalliccontacts, cantilevered fingers, pogo pins, or the like, as isappreciated by one skilled in the art.

In preferred embodiments, the contacts 28 are formed from a conductiveelastomeric material, such as a carbon black elastomer, through whichthe sensor 32 extends (see FIGS. 10B and 11B). Conductive elastomers areadvantageously employed because their resilient properties create anatural compression against mutually engaging contacts, forming a securepress fit therewith. In some embodiments, conductive elastomers can bemolded in such a way that pressing the elastomer against the adjacentcontact performs a wiping action on the surface of the contact, therebycreating a cleaning action during initial connection. Additionally, inpreferred embodiments, the sensor 32 extends through the contacts 28wherein the sensor is electrically and mechanically secure by therelaxation of elastomer around the sensor (see FIGS. 7A to 7D).

In an alternative embodiment, a conductive, stiff plastic forms thecontacts, which are shaped to comply upon application of pressure (forexample, a leaf-spring shape). Contacts of such a configuration can beused instead of a metallic spring, for example, and advantageously avoidthe need for crimping or soldering through compliant materials;additionally, a wiping action can be incorporated into the design toremove contaminants from the surfaces during connection. Non-metalliccontacts can be advantageous because of their seamlessmanufacturability, robustness to thermal compression, non-corrosivesurfaces, and native resistance to electrostatic discharge (ESD) damagedue to their higher-than-metal resistance.

FIGS. 4B and 4C are perspective views of alternative contactconfigurations. FIG. 4B is an illustration of a narrow contactconfiguration. FIG. 4C is an illustration of a wide contactconfiguration. One skilled in the art appreciates that a variety ofconfigurations are suitable for the contacts of the preferredembodiments, whether elastomeric, stiff plastic, or other materials areused. In some circumstances, it can be advantageous to provide multiplecontact configurations (such as illustrated in FIGS. 4A to 4C) todifferentiate sensors from each other. In other words, the architectureof the contacts can include one or more configurations each designed(keyed) to fit with a particular electronics unit. See section entitled“Differentiation of Sensor Systems” below, which describes systems andmethods for differentiating (keying) sensor systems.

Sensor

Preferably, the sensor 32 includes a distal portion 42, also referred toas the in vivo portion, adapted to extend out of the mounting unit forinsertion under the host's skin, and a proximal portion 40, alsoreferred to as an ex vivo portion, adapted to remain above the host'sskin after sensor insertion and to operably connect to the electronicsunit 16 via contacts 28. Preferably, the sensor 32 includes two or moreelectrodes: a working electrode 44 and at least one additionalelectrode, which can function as a counter electrode and/or referenceelectrode, hereinafter referred to as the reference electrode 46. Amembrane system is preferably deposited over the electrodes, such asdescribed in more detail with reference to FIGS. 5A to 5C, below.

FIG. 5A is an expanded cutaway view of a proximal portion 40 of thesensor in one embodiment, showing working and reference electrodes. Inthe illustrated embodiments, the working and reference electrodes 44, 46extend through the contacts 28 to form electrical connection therewith(see FIGS. 10B and 11B). Namely, the working electrode 44 is inelectrical contact with one of the contacts 28 and the referenceelectrode 46 is in electrical contact with the other contact 28, whichin turn provides for electrical connection with the electronics unit 16when it is mated with the mounting unit 14. Mutually engaging electricalcontacts permit operable connection of the sensor 32 to the electronicsunit 16 when connected to the mounting unit 14; however other methods ofelectrically connecting the electronics unit 16 to the sensor 32 arealso possible. In some alternative embodiments, for example, thereference electrode can be configured to extend from the sensor andconnect to a contact at another location on the mounting unit (e.g.non-coaxially). Detachable connection between the mounting unit 14 andelectronics unit 16 provides improved manufacturability, namely, therelatively inexpensive mounting unit 14 can be disposed of whenreplacing the sensor system after its usable life, while the relativelymore expensive electronics unit 16 can be reused with multiple sensorsystems.

In alternative embodiments, the contacts 28 are formed into a variety ofalternative shapes and/or sizes. For example, the contacts 28 can bediscs, spheres, cuboids, and the like. Furthermore, the contacts 28 canbe designed to extend from the mounting unit in a manner that causes aninterference fit within a mating cavity or groove of the electronicsunit, forming a stable mechanical and electrical connection therewith.

FIG. 5B is an expanded cutaway view of a distal portion of the sensor inone embodiment, showing working and reference electrodes. In preferredembodiments, the sensor is formed from a working electrode 44 and areference electrode 46 helically wound around the working electrode 44.An insulator 45 is disposed between the working and reference electrodesto provide necessary electrical insulation therebetween. Certainportions of the electrodes are exposed to enable electrochemicalreaction thereon, for example, a window 43 can be formed in theinsulator to expose a portion of the working electrode 44 forelectrochemical reaction.

In preferred embodiments, each electrode is formed from a fine wire witha diameter of from about 0.001 or less to about 0.010 inches or more,for example, and is formed from, e.g. a plated insulator, a plated wire,or bulk electrically conductive material. Although the illustratedelectrode configuration and associated text describe one preferredmethod of forming a transcutaneous sensor, a variety of knowntranscutaneous sensor configurations can be employed with thetranscutaneous analyte sensor system of the preferred embodiments, suchas are described in U.S. Pat. No. 6,695,860 to Ward et al., U.S. Pat.No. 6,565,509 to Say et al., U.S. Pat. No. 6,248,067 to Causey III, etal., and U.S. Pat. No. 6,514,718 to Heller et al.

In preferred embodiments, the working electrode comprises a wire formedfrom a conductive material, such as platinum, platinum-iridium,palladium, graphite, gold, carbon, conductive polymer, alloys, or thelike. Although the electrodes can by formed by a variety ofmanufacturing techniques (bulk metal processing, deposition of metalonto a substrate, or the like), it can be advantageous to form theelectrodes from plated wire (e.g. platinum on steel wire) or bulk metal(e.g. platinum wire). It is believed that electrodes formed from bulkmetal wire provide superior performance (e.g. in contrast to depositedelectrodes), including increased stability of assay, simplifiedmanufacturability, resistance to contamination (e.g. which can beintroduced in deposition processes), and improved surface reaction (e.g.due to purity of material) without peeling or delamination.

The working electrode 44 is configured to measure the concentration ofan analyte. In an enzymatic electrochemical sensor for detectingglucose, for example, the working electrode measures the hydrogenperoxide produced by an enzyme catalyzed reaction of the analyte beingdetected and creates a measurable electronic current For example, in thedetection of glucose wherein glucose oxidase produces hydrogen peroxideas a byproduct, hydrogen peroxide reacts with the surface of the workingelectrode producing two protons (2H⁺), two electrons (2e⁻) and onemolecule of oxygen (O₂), which produces the electronic current beingdetected.

In preferred embodiments, the working electrode 44 is covered with aninsulating material 45, for example, a non-conductive polymer.Dip-coating, spray-coating, vapor-deposition, or other coating ordeposition techniques can be used to deposit the insulating material onthe working electrode. In one embodiment, the insulating materialcomprises parylene, which can be an advantageous polymer coating for itsstrength, lubricity, and electrical insulation properties. Generally,parylene is produced by vapor deposition and polymerization ofpara-xylylene (or its substituted derivatives). While not wishing to bebound by theory, it is believed that the lubricious coating (e.g.,parylene) on the sensors of the preferred embodiments contributes tominimal trauma and extended sensor life. FIG. 23 shows transcutaneousglucose sensor data and corresponding blood glucose values overapproximately seven days in a human, wherein the transcutaneous glucosesensor data was formed with a parylene coating on at least a portion ofthe device. While parylene coatings are generally preferred, anysuitable insulating material can be used, for example, fluorinatedpolymers, polyethyleneterephthalate, polyurethane, polyimide, othernonconducting polymers, or the like. Glass or ceramic materials can alsobe employed. Other materials suitable for use include surface energymodified coating systems such as are marketed under the trade namesAMC18, AMC148, AMC141, and AMC321 by Advanced Materials ComponentsExpress of Bellafonte, Pa. In some alternative embodiments, however, theworking electrode may not require a coating of insulator.

The reference electrode 46, which can function as a reference electrodealone, or as a dual reference and counter electrode, is formed fromsilver, silver/silver chloride, or the like. Preferably, the referenceelectrode 46 is juxtapositioned and/or twisted with or around theworking electrode 44; however other configurations are also possible(e.g., an intradermal or on-skin reference electrode). In theillustrated embodiments, the reference electrode 46 is helically woundaround the working electrode 44. The assembly of wires is thenoptionally coated or adhered together with an insulating material,similar to that described above, so as to provide an insulatingattachment.

In some embodiments, a silver wire is formed onto the sensor asdescribed above, and subsequently chloridized to form silver/silverchloride reference electrode. Advantageously, chloridizing the silverwire as described herein enables the manufacture of a referenceelectrode with optimal in vivo performance. Namely, by controlling thequantity and amount of chloridization of the silver to formsilver/silver chloride, improved break-in time, stability of thereference electrode, and extended life has been shown with the preferredembodiments (see FIGS. 22 and 23). Additionally, use of silver chlorideas described above allows for relatively inexpensive and simplemanufacture of the reference electrode.

In embodiments wherein an outer insulator is disposed, a portion of thecoated assembly structure can be stripped or otherwise removed, forexample, by hand, excimer lasing, chemical etching, laser ablation,grit-blasting (e.g. with sodium bicarbonate or other suitable grit), orthe like, to expose the electroactive surfaces. Alternatively, a portionof the electrode can be masked prior to depositing the insulator inorder to maintain an exposed electroactive surface area. In oneexemplary embodiment, grit blasting is implemented to expose theelectroactive surfaces, preferably utilizing a grit material that issufficiently hard to ablate the polymer material, while beingsufficiently soft so as to minimize or avoid damage to the underlyingmetal electrode (e.g. a platinum electrode). Although a variety of“grit” materials can be used (e.g. sand, talc, walnut shell, groundplastic, sea salt, and the like), in some preferred embodiments, sodiumbicarbonate is an advantageous grit-material because it is sufficientlyhard to ablate, e.g. a parylene coating without damaging, e.g. anunderlying platinum conductor. One additional advantage of sodiumbicarbonate blasting includes its polishing action on the metal as itstrips the polymer layer, thereby eliminating a cleaning step that mightotherwise be necessary.

In the embodiment illustrated in FIG. 5B, a radial window 43 is formedthrough the insulating material 45 to expose a circumferentialelectroactive surface of the working electrode. Additionally, sections41 of electroactive surface of the reference electrode are exposed. Forexample, the 41 sections of electroactive surface can be masked duringdeposition of an outer insulating layer or etched after deposition of anouter insulating layer.

In some applications, cellular attack or migration of cells to thesensor can cause reduced sensitivity and/or function of the device,particularly after the first day of implantation. However, when theexposed electroactive surface is distributed circumferentially about thesensor (e.g. as in a radial window), the available surface area forreaction can be sufficiently distributed so as to minimize the effect oflocal cellular invasion of the sensor on the sensor signal.Alternatively, a tangential exposed electroactive window can be formed,for example, by stripping only one side of the coated assemblystructure. In other alternative embodiments, the window can be providedat the tip of the coated assembly structure such that the electroactivesurfaces are exposed at the tip of the sensor. Other methods andconfigurations for exposing electroactive surfaces can also be employed.

In some embodiments, the working electrode has a diameter of from about0.001 inches or less to about 0.010 inches or more, preferably fromabout 0.002 inches to about 0.008 inches, and more preferably from about0.004 inches to about 0.005 inches. The length of the window can be fromabout 0.1 mm (about 0.004 inches) or less to about 2 mm (about 0.078inches) or more, and preferably from about 0.5 mm (about 0.02 inches) toabout 0.75 mm (0.03 inches). In such embodiments, the exposed surfacearea of the working electrode is preferably from about 0.000013 in²(0.0000839 cm²) or less to about 0.0025 in² (0.016129 cm²) or more(assuming a diameter of from about 0.001 inches to about 0.010 inchesand a length of from about 0.004 inches to about 0.078 inches). Thepreferred exposed surface area of the working electrode is selected toproduce an analyte signal with a current in the picoAmp range, such asis described in more detail elsewhere herein. However, a current in thepicoAmp range can be dependent upon a variety of factors, for examplethe electronic circuitry design (e.g. sample rate, current draw, A/Dconverter bit resolution, etc.), the membrane system (e.g. permeabilityof the analyte through the membrane system), and the exposed surfacearea of the working electrode. Accordingly, the exposed electroactiveworking electrode surface area can be selected to have a value greaterthan or less than the above-described ranges taking into considerationalterations in the membrane system and/or electronic circuitry. Inpreferred embodiments of a glucose sensor, it can be advantageous tominimize the surface area of the working electrode while maximizing thediffusivity of glucose in order to optimize the signal-to-noise ratiowhile maintaining sensor performance in both high and low glucoseconcentration ranges.

In some alternative embodiments, the exposed surface area of the working(and/or other) electrode can be increased by altering the cross-sectionof the electrode itself. For example, in some embodiments thecross-section of the working electrode can be defined by a cross, star,cloverleaf, ribbed, dimpled, ridged, irregular, or other non-circularconfiguration; thus, for any predetermined length of electrode, aspecific increased surface area can be achieved (as compared to the areaachieved by a circular cross-section). Increasing the surface area ofthe working electrode can be advantageous in providing an increasedsignal responsive to the analyte concentration, which in turn can behelpful in improving the signal-to-noise ratio, for example.

In some alternative embodiments, additional electrodes can be includedwithin the assembly, for example, a three-electrode system (working,reference, and counter electrodes) and/or an additional workingelectrode (e.g. an electrode which can be used to generate oxygen, whichis configured as a baseline subtracting electrode, or which isconfigured for measuring additional analytes). U.S. Pat. No. 7,081,195and U.S. Patent Publication No. US-2005-0143635-A1 describe some systemsand methods for implementing and using additional working, counter,and/or reference electrodes. In one implementation wherein the sensorcomprises two working electrodes, the two working electrodes arejuxtapositioned (e.g. extend parallel to each other), around which thereference electrode is disposed (e.g. helically wound). In someembodiments wherein two or more working electrodes are provided, theworking electrodes can be formed in a double-, triple-, quad-, etc.helix configuration along the length of the sensor (for example,surrounding a reference electrode, insulated rod, or other supportstructure). The resulting electrode system can be configured with anappropriate membrane system, wherein the first working electrode isconfigured to measure a first signal comprising glucose and baseline andthe additional working electrode is configured to measure a baselinesignal consisting of baseline only (e.g. configured to be substantiallysimilar to the first working electrode without an enzyme disposedthereon). In this way, the baseline signal can be subtracted from thefirst signal to produce a glucose-only signal that is substantially notsubject to fluctuations in the baseline and/or interfering species onthe signal.

Although the preferred embodiments illustrate one electrodeconfiguration including one bulk metal wire helically wound aroundanother bulk metal wire, other electrode configurations are alsocontemplated. In an alternative embodiment, the working electrodecomprises a tube with a reference electrode disposed or coiled inside,including an insulator therebetween. Alternatively, the referenceelectrode comprises a tube with a working electrode disposed or coiledinside, including an insulator therebetween. In another alternativeembodiment, a polymer (e.g. insulating) rod is provided, wherein theelectrodes are deposited (e.g. electro-plated) thereon. In yet anotheralternative embodiment, a metallic (e.g. steel) rod is provided, coatedwith an insulating material, onto which the working and referenceelectrodes are deposited. In yet another alternative embodiment, one ormore working electrodes are helically wound around a referenceelectrode.

Preferably, the electrodes and membrane systems of the preferredembodiments are coaxially formed, namely, the electrodes and/or membranesystem all share the same central axis. While not wishing to be bound bytheory, it is believed that a coaxial design of the sensor enables asymmetrical design without a preferred bend radius. Namely, in contrastto prior art sensors comprising a substantially planar configurationthat can suffer from regular bending about the plane of the sensor, thecoaxial design of the preferred embodiments do not have a preferred bendradius and therefore are not subject to regular bending about aparticular plane (which can cause fatigue failures and the like).However, non-coaxial sensors can be implemented with the sensor systemof the preferred embodiments.

In addition to the above-described advantages, the coaxial sensor designof the preferred embodiments enables the diameter of the connecting endof the sensor (proximal portion) to be substantially the same as that ofthe sensing end (distal portion) such that the needle is able to insertthe sensor into the host and subsequently slide back over the sensor andrelease the sensor from the needle, without slots or other complexmulti-component designs.

In one such alternative embodiment, the two wires of the sensor are heldapart and configured for insertion into the host in proximal butseparate locations. The separation of the working and referenceelectrodes in such an embodiment can provide additional electrochemicalstability with simplified manufacture and electrical connectivity. It isappreciated by one skilled in the art that a variety of electrodeconfigurations can be implemented with the preferred embodiments.

In some embodiments, the sensor includes an antimicrobial portionconfigured to extend through the exit-site when the sensor is implantedin the host. Namely, the sensor is designed with in vivo and ex vivoportions as described in more detail elsewhere herein; additionally, thesensor comprises a transition portion, also referred to as anantimicrobial portion, located between the in vivo and ex vivo portions42, 40. The antimicrobial portion is designed to provide antimicrobialeffects to the exit-site and adjacent tissue when implanted in the host.

In some embodiments, the antimicrobial portion comprises silver, e.g.,the portion of a silver reference electrode that is configured to extendthrough the exit-site when implanted. Although exit-site infections area common adverse occurrence associated with some conventionaltranscutaneous medical devices, the devices of preferred embodiments aredesigned at least in part to minimize infection, to minimize irritation,and/or to extend the duration of implantation of the sensor by utilizinga silver reference electrode to extend through the exit-site whenimplanted in a patient. While not wishing to be bound by theory, it isbelieved that the silver may reduce local tissue infections (within thetissue and at the exit-site); namely, steady release of molecularquantities of silver is believed to have an antimicrobial effect inbiological tissue (e.g., reducing or preventing irritation andinfection), also referred to as passive antimicrobial effects. Althoughone example of passive antimicrobial effects is described herein, oneskilled in the art can appreciate a variety of passive anti-microbialsystems and methods that can be implemented with the preferredembodiments. Additionally, it is believed that antimicrobial effects cancontribute to extended life of a transcutaneous analyte sensor, enablinga functional lifetime past a few days, e.g., seven days or longer. FIG.23 shows transcutaneous glucose sensor data and corresponding bloodglucose values over approximately seven days in a human, wherein thetranscutaneous glucose sensor data was formed with a silver transitionportion that extended through the exit-site after sensor implantation.

In some embodiments, active antimicrobial systems and methods areprovided in the sensor system in order to further enhance theantimicrobial effects at the exit-site. In one such embodiment, anauxiliary silver wire is disposed on or around the sensor, wherein theauxiliary silver wire is connected to electronics and configured to passa current sufficient to enhance its antimicrobial properties (activeantimicrobial effects), as is appreciated by one skilled in the art. Thecurrent can be passed continuously or intermittently, such thatsufficient antimicrobial properties are provided. Although one exampleof active antimicrobial effects is described herein, one skilled in theart can appreciate a variety of active anti-microbial systems andmethods that can be implemented with the preferred embodiments.

Anchoring Mechanism

It is preferred that the sensor remains substantially stationary withinthe tissue of the host, such that migration or motion of the sensor withrespect to the surrounding tissue is minimized. Migration or motion isbelieved to cause inflammation at the sensor implant site due toirritation, and can also cause noise on the sensor signal due tomotion-related artifact, for example. Therefore, it can be advantageousto provide an anchoring mechanism that provides support for the sensor'sin vivo portion to avoid the above-mentioned problems. Combiningadvantageous sensor geometry with an advantageous anchoring minimizesadditional parts and allows for an optimally small or low profile designof the sensor. In one embodiment the sensor includes a surfacetopography, such as the helical surface topography provided by thereference electrode surrounding the working electrode. In alternativeembodiments, a surface topography could be provided by a roughenedsurface, porous surface (e.g. porous parylene), ridged surface, or thelike. Additionally (or alternatively), the anchoring can be provided byprongs, spines, barbs, wings, hooks, a bulbous portion (for example, atthe distal end), an S-bend along the sensor, a rough surface topography,a gradually changing diameter, combinations thereof, or the like, whichcan be used alone or in combination with the helical surface topographyto stabilize the sensor within the subcutaneous tissue.

Variable Stiffness

As described above, conventional transcutaneous devices are believed tosuffer from motion artifact associated with host movement when the hostis using the device. For example, when a transcutaneous analyte sensoris inserted into the host, various movements on the sensor (for example,relative movement within and between the subcutaneous space, dermis,skin, and external portions of the sensor) create stresses on thedevice, which is known to produce artifacts on the sensor signal.Accordingly, there are different design considerations (for example,stress considerations) on various sections of the sensor. For example,the distal portion 42 of the sensor can benefit in general from greaterflexibility as it encounters greater mechanical stresses caused bymovement of the tissue within the patient and relative movement betweenthe in vivo and ex vivo portions of the sensor. On the other hand, theproximal portion 40 of the sensor can benefit in general from a stiffer,more robust design to ensure structural integrity and/or reliableelectrical connections. Additionally, in some embodiments wherein aneedle is retracted over the proximal portion 40 of the device (seeFIGS. 6 to 8), a stiffer design can minimize crimping of the sensorand/or ease in retraction of the needle from the sensor. Thus, bydesigning greater flexibility into the in vivo (distal) portion 42, theflexibility is believed to compensate for patient movement, and noiseassociated therewith. By designing greater stiffness into the ex vivo(proximal) portion 40, column strength (for retraction of the needleover the sensor), electrical connections, and integrity can be enhanced.In some alternative embodiments, a stiffer distal end and/or a moreflexible proximal end can be advantageous as described in U.S.Publication No. US-2006-0015024-A1.

The preferred embodiments provide a distal portion 42 of the sensor 32designed to be more flexible than a proximal portion 40 of the sensor.The variable stiffness of the preferred embodiments can be provided byvariable pitch of any one or more helically wound wires of the device,variable cross-section of any one or more wires of the device, and/orvariable hardening and/or softening of any one or more wires of thedevice, such as is described in more detail with reference to U.S.Publication No. US-2006-0015024-A1.

Membrane System

FIG. 5C is a cross-sectional view through the sensor on line C-C of FIG.5B showing the exposed electroactive surface of the working electrodesurrounded by the membrane system in one embodiment. Preferably, amembrane system is deposited over at least a portion of theelectroactive surfaces of the sensor 32 (working electrode andoptionally reference electrode) and provides protection of the exposedelectrode surface from the biological environment, diffusion resistance(limitation) of the analyte if needed, a catalyst for enabling anenzymatic reaction, limitation or blocking of interferants, and/orhydrophilicity at the electrochemically reactive surfaces of the sensorinterface. Some examples of suitable membrane systems are described inU.S. Patent Publication No. 2005-0245799-A1.

In general, the membrane system includes a plurality of domains, forexample, an electrode domain 47, an interference domain 48, an enzymedomain 49 (for example, including glucose oxidase), and a resistancedomain 50, and can include a high oxygen solubility domain, and/or abioprotective domain (not shown), such as is described in more detail inU.S. Patent Publication No. 2005-0245799-A1, and such as is described inmore detail below. The membrane system can be deposited on the exposedelectroactive surfaces using known thin film techniques (for example,spraying, electro-depositing, dipping, or the like). In one embodiment,one or more domains are deposited by dipping the sensor into a solutionand drawing out the sensor at a speed that provides the appropriatedomain thickness. However, the membrane system can be disposed over (ordeposited on) the electroactive surfaces using any known method as willbe appreciated by one skilled in the art.

Electrode Domain

In some embodiments, the membrane system comprises an optional electrodedomain 47. The electrode domain 47 is provided to ensure that anelectrochemical reaction occurs between the electroactive surfaces ofthe working electrode and the reference electrode, and thus theelectrode domain 47 is preferably situated more proximal to theelectroactive surfaces than the enzyme domain. Preferably, the electrodedomain 47 includes a semipermeable coating that maintains a layer ofwater at the electrochemically reactive surfaces of the sensor, forexample, a humectant in a binder material can be employed as anelectrode domain; this allows for the full transport of ions in theaqueous environment. The electrode domain can also assist in stabilizingthe operation of the sensor by overcoming electrode start-up anddrifting problems caused by inadequate electrolyte. The material thatforms the electrode domain can also protect against pH-mediated damagethat can result from the formation of a large pH gradient due to theelectrochemical activity of the electrodes.

In one embodiment, the electrode domain 47 includes a flexible,water-swellable, hydrogel film having a “dry film” thickness of fromabout 0.05 micron or less to about 20 microns or more, more preferablyfrom about 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1,1.5, 2, 2.5, 3, or 3.5 to about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, or 19.5 microns, and more preferably from about 2,2.5 or 3 microns to about 3.5, 4, 4.5, or 5 microns. “Dry film”thickness refers to the thickness of a cured film cast from a coatingformulation by standard coating techniques.

In certain embodiments, the electrode domain 47 is formed of a curablemixture of a urethane polymer and a hydrophilic polymer. Particularlypreferred coatings are formed of a polyurethane polymer havingcarboxylate functional groups and non-ionic hydrophilic polyethersegments, wherein the polyurethane polymer is crosslinked with a watersoluble carbodiimide (e.g.,1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC)) in the presence ofpolyvinylpyrrolidone and cured at a moderate temperature of about 50° C.

Preferably, the electrode domain 47 is deposited by spray or dip-coatingthe electroactive surfaces of the sensor 32. More preferably, theelectrode domain is formed by dip-coating the electroactive surfaces inan electrode solution and curing the domain for a time of from about 15to about 30 minutes at a temperature of from about 40 to about 55° C.(and can be accomplished under vacuum (e.g., 20 to 30 mmHg)). Inembodiments wherein dip-coating is used to deposit the electrode domain,a preferred insertion rate of from about 1 to about 3 inches per minute,with a preferred dwell time of from about 0.5 to about 2 minutes, and apreferred withdrawal rate of from about 0.25 to about 2 inches perminute provide a functional coating. However, values outside of thoseset forth above can be acceptable or even desirable in certainembodiments, for example, dependent upon viscosity and surface tensionas is appreciated by one skilled in the art. In one embodiment, theelectroactive surfaces of the electrode system are dip-coated one time(one layer) and cured at 50° C. under vacuum for 20 minutes.

Although an independent electrode domain is described herein, in someembodiments, sufficient hydrophilicity can be provided in theinterference domain and/or enzyme domain (the domain adjacent to theelectroactive surfaces) so as to provide for the full transport of ionsin the aqueous environment (e.g. without a distinct electrode domain).

Interference Domain

In some embodiments, an optional interference domain 48 is provided,which generally includes a polymer domain that restricts the flow of oneor more interferants. In some embodiments, the interference domain 48functions as a molecular sieve that allows analytes and other substancesthat are to be measured by the electrodes to pass through, whilepreventing passage of other substances, including interferants such asascorbate and urea (see U.S. Pat. No. 6,001,067 to Shults). Some knowninterferants for a glucose-oxidase based electrochemical sensor includeacetaminophen, ascorbic acid, bilirubin, cholesterol, creatinine,dopamine, ephedrine, ibuprofen, L-dopa, methyldopa, salicylate,tetracycline, tolazamide, tolbutamide, triglycerides, and uric acid.

Several polymer types that can be utilized as a base material for theinterference domain 48 include polyurethanes, polymers having pendantionic groups, and polymers having controlled pore size, for example. Inone embodiment, the interference domain includes a thin, hydrophobicmembrane that is non-swellable and restricts diffusion of low molecularweight species. The interference domain 48 is permeable to relativelylow molecular weight substances, such as hydrogen peroxide, butrestricts the passage of higher molecular weight substances, includingglucose and ascorbic acid. Other systems and methods for reducing oreliminating interference species that can be applied to the membranesystem of the preferred embodiments are described in U.S. Pat. No.7,074,307, U.S. Patent Publication No. US-2005-0176136-A1, U.S. Pat. No.7,081,195 and U.S. Patent Publication No. US-2005-0143635-A1. In somealternative embodiments, a distinct interference domain is not included.

In preferred embodiments, the interference domain 48 is deposited ontothe electrode domain (or directly onto the electroactive surfaces when adistinct electrode domain is not included) for a domain thickness offrom about 0.05 micron or less to about 20 microns or more, morepreferably from about 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45,0.5, 1, 1.5, 2, 2.5, 3, or 3.5 to about 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, or 19.5 microns, and more preferably fromabout 2, 2.5 or 3 microns to about 3.5, 4, 4.5, or 5 microns. Thickermembranes can also be useful, but thinner membranes are generallypreferred because they have a lower impact on the rate of diffusion ofhydrogen peroxide from the enzyme membrane to the electrodes.Unfortunately, the thin thickness of the interference domainsconventionally used can introduce variability in the membrane systemprocessing. For example, if too much or too little interference domainis incorporated within a membrane system, the performance of themembrane can be adversely affected.

Enzyme Domain

In preferred embodiments, the membrane system further includes an enzymedomain 49 disposed more distally situated from the electroactivesurfaces than the interference domain 48 (or electrode domain 47 when adistinct interference is not included). In some embodiments, the enzymedomain is directly deposited onto the electroactive surfaces (whenneither an electrode or interference domain is included). In thepreferred embodiments, the enzyme domain 49 provides an enzyme tocatalyze the reaction of the analyte and its co-reactant, as describedin more detail below. Preferably, the enzyme domain includes glucoseoxidase; however other oxidases, for example, galactose oxidase oruricase oxidase, can also be used.

For an enzyme-based electrochemical glucose sensor to perform well, thesensor's response is preferably limited by neither enzyme activity norco-reactant concentration. Because enzymes, including glucose oxidase,are subject to deactivation as a function of time even in ambientconditions, this behavior is compensated for in forming the enzymedomain. Preferably, the enzyme domain 49 is constructed of aqueousdispersions of colloidal polyurethane polymers including the enzyme.However, in alternative embodiments the enzyme domain is constructedfrom an oxygen enhancing material, for example, silicone, orfluorocarbon, in order to provide a supply of excess oxygen duringtransient ischemia. Preferably, the enzyme is immobilized within thedomain. See U.S. Patent Publication No. US-2005-0054909-A1.

In preferred embodiments, the enzyme domain 49 is deposited onto theinterference domain for a domain thickness of from about 0.05 micron orless to about 20 microns or more, more preferably from about 0.05, 0.1,0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1, 1.5, 2, 2.5, 3, or 3.5 toabout 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 19.5microns, and more preferably from about 2, 2.5 or 3 microns to about3.5, 4, 4.5, or 5 microns. However in some embodiments, the enzymedomain is deposited onto the electrode domain or directly onto theelectroactive surfaces. Preferably, the enzyme domain 49 is deposited byspray or dip coating. More preferably, the enzyme domain is formed bydip-coating the electrode domain into an enzyme domain solution andcuring the domain for from about 15 to about 30 minutes at a temperatureof from about 40 to about 55° C. (and can be accomplished under vacuum(e.g. 20 to 30 mmHg)). In embodiments wherein dip-coating is used todeposit the enzyme domain at room temperature, a preferred insertionrate of from about 1 inch per minute to about 3 inches per minute, witha preferred dwell time of from about 0.5 minutes to about 2 minutes, anda preferred withdrawal rate of from about 0.25 inch per minute to about2 inches per minute provide a functional coating. However, valuesoutside of those set forth above can be acceptable or even desirable incertain embodiments, for example, dependent upon viscosity and surfacetension as is appreciated by one skilled in the art. In one embodiment,the enzyme domain 49 is formed by dip coating two times (namely, formingtwo layers) in a coating solution and curing at 50° C. under vacuum for20 minutes. However, in some embodiments, the enzyme domain can beformed by dip-coating and/or spray-coating one or more layers at apredetermined concentration of the coating solution, insertion rate,dwell time, withdrawal rate, and/or desired thickness.

Resistance Domain

In preferred embodiments, the membrane system includes a resistancedomain 50 disposed more distal from the electroactive surfaces than theenzyme domain 49. Although the following description is directed to aresistance domain for a glucose sensor, the resistance domain can bemodified for other analytes and co-reactants as well.

There exists a molar excess of glucose relative to the amount of oxygenin blood; that is, for every free oxygen molecule in extracellularfluid, there are typically more than 100 glucose molecules present (seeUpdike et al., Diabetes Care 5:207-21(1982)). However, an immobilizedenzyme-based glucose sensor employing oxygen as co-reactant ispreferably supplied with oxygen in non-rate-limiting excess in order forthe sensor to respond linearly to changes in glucose concentration,while not responding to changes in oxygen concentration. Specifically,when a glucose-monitoring reaction is oxygen limited, linearity is notachieved above minimal concentrations of glucose. Without asemipermeable membrane situated over the enzyme domain to control theflux of glucose and oxygen, a linear response to glucose levels can beobtained only for glucose concentrations of up to about 40 mg/dL.However, in a clinical setting, a linear response to glucose levels isdesirable up to at least about 400 mg/dL.

The resistance domain 50 includes a semi permeable membrane thatcontrols the flux of oxygen and glucose to the underlying enzyme domain49, preferably rendering oxygen in a non-rate-limiting excess. As aresult, the upper limit of linearity of glucose measurement is extendedto a much higher value than that which is achieved without theresistance domain. In one embodiment, the resistance domain 50 exhibitsan oxygen to glucose permeability ratio of from about 50:1 or less toabout 400:1 or more, preferably about 200:1. As a result,one-dimensional reactant diffusion is adequate to provide excess oxygenat all reasonable glucose and oxygen concentrations found in thesubcutaneous matrix (See Rhodes et al., Anal. Chem., 66:1520-1529(1994)).

In alternative embodiments, a lower ratio of oxygen-to-glucose can besufficient to provide excess oxygen by using a high oxygen solubilitydomain (for example, a silicone or fluorocarbon-based material ordomain) to enhance the supply/transport of oxygen to the enzyme domain49. If more oxygen is supplied to the enzyme, then more glucose can alsobe supplied to the enzyme without creating an oxygen rate-limitingexcess. In alternative embodiments, the resistance domain is formed froma silicone composition, such as is described in U.S. Patent PublicationNo. US-2005-0090607-A1.

In a preferred embodiment, the resistance domain 50 includes apolyurethane membrane with both hydrophilic and hydrophobic regions tocontrol the diffusion of glucose and oxygen to an analyte sensor, themembrane being fabricated easily and reproducibly from commerciallyavailable materials. A suitable hydrophobic polymer component is apolyurethane, or polyetherurethaneurea. Polyurethane is a polymerproduced by the condensation reaction of a diisocyanate and adifunctional hydroxyl-containing material. A polyurethaneurea is apolymer produced by the condensation reaction of a diisocyanate and adifunctional amine-containing material. Preferred diisocyanates includealiphatic diisocyanates containing from about 4 to about 8 methyleneunits. Diisocyanates containing cycloaliphatic moieties can also beuseful in the preparation of the polymer and copolymer components of themembranes of preferred embodiments. The material that forms the basis ofthe hydrophobic matrix of the resistance domain can be any of thoseknown in the art as appropriate for use as membranes in sensor devicesand as having sufficient permeability to allow relevant compounds topass through it, for example, to allow an oxygen molecule to passthrough the membrane from the sample under examination in order to reachthe active enzyme or electrochemical electrodes. Examples of materialswhich can be used to make non-polyurethane type membranes include vinylpolymers, polyethers, polyesters, polyamides, inorganic polymers such aspolysiloxanes and polycarbosiloxanes, natural polymers such ascellulosic and protein based materials, and mixtures or combinationsthereof.

In a preferred embodiment, the hydrophilic polymer component ispolyethylene oxide. For example, one useful hydrophobic-hydrophiliccopolymer component is a polyurethane polymer that includes about 20%hydrophilic polyethylene oxide. The polyethylene oxide portions of thecopolymer are thermodynamically driven to separate from the hydrophobicportions of the copolymer and the hydrophobic polymer component. The 20%polyethylene oxide-based soft segment portion of the copolymer used toform the final blend affects the water pick-up and subsequent glucosepermeability of the membrane.

In preferred embodiments, the resistance domain 50 is deposited onto theenzyme domain 49 to yield a domain thickness of from about 0.05 micronsor less to about 20 microns or more, more preferably from about 0.05,0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1, 1.5, 2, 2.5, 3, or3.5 to about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,or 19.5 microns, and more preferably from about 2, 2.5, or 3 microns toabout 3.5, 4, 4.5, or 5 microns. Preferably, the resistance domain isdeposited onto the enzyme domain by spray coating or dip coating. Incertain embodiments, spray coating is the preferred depositiontechnique. The spraying process atomizes and mists the solution, andtherefore most or all of the solvent is evaporated prior to the coatingmaterial settling on the underlying domain, thereby minimizing contactof the solvent with the enzyme. One additional advantage ofspray-coating the resistance domain as described in the preferredembodiments includes formation of a membrane system that substantiallyblocks or resists ascorbate (a known electrochemical interferant inhydrogen peroxide-measuring glucose sensors). While not wishing to bebound by theory, it is believed that during the process of depositingthe resistance domain as described in the preferred embodiments, astructural morphology is formed, characterized in that ascorbate doesnot substantially permeate therethrough.

In preferred embodiments, the resistance domain 50 is deposited on theenzyme domain 49 by spray-coating a solution of from about 1 wt. % toabout 5 wt. % polymer and from about 95 wt. % to about 99 wt. % solvent.In spraying a solution of resistance domain material, including asolvent, onto the enzyme domain, it is desirable to mitigate orsubstantially reduce any contact with enzyme of any solvent in the spraysolution that can deactivate the underlying enzyme of the enzyme domain49. Tetrahydrofuran (THF) is one solvent that minimally or negligiblyaffects the enzyme of the enzyme domain upon spraying. Other solventscan also be suitable for use, as is appreciated by one skilled in theart.

Although a variety of spraying or deposition techniques can be used,spraying the resistance domain material and rotating the sensor at leastone time by 180° can provide adequate coverage by the resistance domain.Spraying the resistance domain material and rotating the sensor at leasttwo times by 120 degrees provides even greater coverage (one layer of360° coverage), thereby ensuring resistivity to glucose, such as isdescribed in more detail above.

In preferred embodiments, the resistance domain 50 is spray-coated andsubsequently cured for a time of from about 15 to about 90 minutes at atemperature of from about 40 to about 60° C. (and can be accomplishedunder vacuum (e.g. 20 to 30 mmHg)). A cure time of up to about 90minutes or more can be advantageous to ensure complete drying of theresistance domain. While not wishing to be bound by theory, it isbelieved that complete drying of the resistance domain aids instabilizing the sensitivity of the glucose sensor signal. It reducesdrifting of the signal sensitivity over time, and complete drying isbelieved to stabilize performance of the glucose sensor signal in loweroxygen environments.

In one embodiment, the resistance domain 50 is formed by spray-coatingat least six layers (namely, rotating the sensor seventeen times by 120°for at least six layers of 360° coverage) and curing at 50° C. undervacuum for 60 minutes. However, the resistance domain can be formed bydip-coating or spray-coating any layer or plurality of layers, dependingupon the concentration of the solution, insertion rate, dwell time,withdrawal rate, and/or the desired thickness of the resulting film.

Advantageously, sensors with the membrane system of the preferredembodiments, including an electrode domain 47 and/or interference domain48, an enzyme domain 49, and a resistance domain 50, provide stablesignal response to increasing glucose levels of from about 40 to about400 mg/dL, and sustained function (at least 90% signal strength) even atlow oxygen levels (for example, at about 0.6 mg/L O₂). While not wishingto be bound by theory, it is believed that the resistance domainprovides sufficient resistivity, or the enzyme domain providessufficient enzyme, such that oxygen limitations are seen at a much lowerconcentration of oxygen as compared to prior art sensors.

In preferred embodiments, a sensor signal with a current in the picoAmprange is preferred, which is described in more detail elsewhere herein.However, the ability to produce a signal with a current in the picoAmprange can be dependent upon a combination of factors, including theelectronic circuitry design (e.g. A/D converter, bit resolution, and thelike), the membrane system (e.g. permeability of the analyte through theresistance domain, enzyme concentration, and/or electrolyte availabilityto the electrochemical reaction at the electrodes), and the exposedsurface area of the working electrode. For example, the resistancedomain can be designed to be more or less restrictive to the analytedepending upon to the design of the electronic circuitry, membranesystem, and/or exposed electroactive surface area of the workingelectrode.

Accordingly, in preferred embodiments, the membrane system is designedwith a sensitivity of from about 1 pA/mg/dL to about 100 pA/mg/dL,preferably from about 5 pA/mg/dL to about 25 pA/mg/dL, and morepreferably from about 4 pA/mg/dL to about 7 pA/mg/dL. While not wishingto be bound by any particular theory, it is believed that membranesystems designed with a sensitivity in the preferred ranges permitmeasurement of the analyte signal in low analyte and/or low oxygensituations. Namely, conventional analyte sensors have shown reducedmeasurement accuracy in low analyte ranges due to lower availability ofthe analyte to the sensor and/or have shown increased signal noise inhigh analyte ranges due to insufficient oxygen necessary to react withthe amount of analyte being measured. While not wishing to be bound bytheory, it is believed that the membrane systems of the preferredembodiments, in combination with the electronic circuitry design andexposed electrochemical reactive surface area design, supportmeasurement of the analyte in the picoAmp range, which enables animproved level of resolution and accuracy in both low and high analyteranges not seen in the prior art.

Mutarotase Enzyme

In some embodiments, mutarotase, an enzyme that converts α D-glucose toβ D-glucose, is incorporated into the membrane system. Mutarotase can beincorporated into the enzyme domain and/or can be incorporated intoanother domain of the membrane system. In general, glucose exists in twodistinct isomers, α and β, which are in equilibrium with one another insolution and in the blood or interstitial fluid. At equilibrium, α ispresent at a relative concentration of about 35.5% and β is present inthe relative concentration of about 64.5% (see Okuda et al., AnalBiochem. 1971 September; 43(1):312-5). Glucose oxidase, which is aconventional enzyme used to react with glucose in glucose sensors,reacts with β D-glucose and not with α D-glucose. Since only the βD-glucose isomer reacts with the glucose oxidase, errant readings mayoccur in a glucose sensor responsive to a shift of the equilibriumbetween the α D-glucose and the β D-glucose. Many compounds, such ascalcium, can affect equilibrium shifts of α D-glucose and β D-glucose.For example, as disclosed in U.S. Pat. No. 3,964,974 to Banaugh et al.,compounds that exert a mutarotation accelerating effect on α D-glucoseinclude histidine, aspartic acid, imidazole, glutamic acid, α hydroxylpyridine, and phosphate.

Accordingly, a shift in α D-glucose and β D-glucose equilibrium cancause a glucose sensor based on glucose oxidase to err high or low. Toovercome the risks associated with errantly high or low sensor readingsdue to equilibrium shifts, the sensor of the preferred embodiments canbe configured to measure total glucose in the host, including αD-glucose and β D-glucose by the incorporation of the mutarotase enzyme,which converts α D-glucose to β D-glucose.

Although sensors of some embodiments described herein include anoptional interference domain in order to block or reduce one or moreinterferants, sensors with the membrane systems of the preferredembodiments, including an electrode domain 47, an enzyme domain 48, anda resistance domain 49, have been shown to inhibit ascorbate without anadditional interference domain. Namely, the membrane system of thepreferred embodiments, including an electrode domain 47, an enzymedomain 48, and a resistance domain 49, has been shown to besubstantially non-responsive to ascorbate in physiologically acceptableranges. While not wishing to be bound by theory, it is believed that theprocessing process of spraying the depositing the resistance domain byspray coating, as described herein, forms results in a structuralmorphology that is substantially resistance resistant to ascorbate.

Interference-free Membrane Systems

In general, it is believed that appropriate solvents and/or depositionmethods can be chosen for one or more of the domains of the membranesystem that form one or more transitional domains such that interferantsdo not substantially permeate therethrough. Thus, sensors can be builtwithout distinct or deposited interference domains, which arenon-responsive to interferants. While not wishing to be bound by theory,it is believed that a simplified multilayer membrane system, more robustmultilayer manufacturing process, and reduced variability caused by thethickness and associated oxygen and glucose sensitivity of the depositedmicron-thin interference domain can be provided. Additionally, theoptional polymer-based interference domain, which usually inhibitshydrogen peroxide diffusion, is eliminated, thereby enhancing the amountof hydrogen peroxide that passes through the membrane system.

Oxygen Conduit

As described above, certain sensors depend upon an enzyme within themembrane system through which the host's bodily fluid passes and inwhich the analyte (for example, glucose) within the bodily fluid reactsin the presence of a co-reactant (for example, oxygen) to generate aproduct. The product is then measured using electrochemical methods, andthus the output of an electrode system functions as a measure of theanalyte. For example, when the sensor is a glucose oxidase based glucosesensor, the species measured at the working electrode is H₂O₂. Anenzyme, glucose oxidase, catalyzes the conversion of oxygen and glucoseto hydrogen peroxide and gluconate according to the following reaction:Glucose+O₂→Gluconate+H₂O₂

Because for each glucose molecule reacted there is a proportional changein the product, H₂O₂, one can monitor the change in H₂O₂ to determineglucose concentration. Oxidation of H₂O₂ by the working electrode isbalanced by reduction of ambient oxygen, enzyme generated H₂O₂ and otherreducible species at a counter electrode, for example. See Fraser, D.M., “An Introduction to In Vivo Biosensing: Progress and Problems.” In“Biosensors and the Body,” D. M. Fraser, ed., 1997, pp. 1-56 John Wileyand Sons, New York))

In vivo, glucose concentration is generally about one hundred times ormore that of the oxygen concentration. Consequently, oxygen is alimiting reactant in the electrochemical reaction, and when insufficientoxygen is provided to the sensor, the sensor is unable to accuratelymeasure glucose concentration. Thus, depressed sensor function orinaccuracy is believed to be a result of problems in availability ofoxygen to the enzyme and/or electroactive surface(s).

Accordingly, in an alternative embodiment, an oxygen conduit (forexample, a high oxygen solubility domain formed from silicone orfluorochemicals) is provided that extends from the ex vivo portion ofthe sensor to the in vivo portion of the sensor to increase oxygenavailability to the enzyme. The oxygen conduit can be formed as a partof the coating (insulating) material or can be a separate conduitassociated with the assembly of wires that forms the sensor.

Porous Biointerface Materials

In alternative embodiments, the distal portion 42 includes a porousmaterial disposed over some portion thereof, which modifies the host'stissue response to the sensor. In some embodiments, the porous materialsurrounding the sensor advantageously enhances and extends sensorperformance and lifetime in the short term by slowing or reducingcellular migration to the sensor and associated degradation that wouldotherwise be caused by cellular invasion if the sensor were directlyexposed to the in vivo environment. Alternatively, the porous materialcan provide stabilization of the sensor via tissue ingrowth into theporous material in the long term. Suitable porous materials includesilicone, polytetrafluoroethylene, expanded polytetrafluoroethylene,polyethylene-co-tetrafluoroethylene, polyolefin, polyester,polycarbonate, biostable polytetrafluoroethylene, homopolymers,copolymers, terpolymers of polyurethanes, polypropylene (PP),polyvinylchloride (PVC), polyvinylidene fluoride (PVDF), polyvinylalcohol (PVA), polybutylene terephthalate (PBT), polymethylmethacrylate(PMMA), polyether ether ketone (PEEK), polyamides, polyurethanes,cellulosic polymers, polysulfones and block copolymers thereofincluding, for example, di-block, tri-block, alternating, random andgraft copolymers, as well as metals, ceramics, cellulose, hydrogelpolymers, poly (2-hydroxyethyl methacrylate, pHEMA), hydroxyethylmethacrylate, (HEMA), polyacrylonitrile-polyvinyl chloride (PAN-PVC),high density polyethylene, acrylic copolymers, nylon, polyvinyldifluoride, polyanhydrides, poly(1-lysine), poly (L-lactic acid),hydroxyethylmethacrylate, hydroxyapeptite, alumina, zirconia, carbonfiber, aluminum, calcium phosphate, titanium, titanium alloy, nintinol,stainless steel, and CoCr alloy, or the like, such as are described inU.S. Patent Publication No. US-2005-0031689-A1 and U.S. Pat. No.7,192,450.

In some embodiments, the porous material surrounding the sensor providesunique advantages in the short term (e.g. one to 14 days) that can beused to enhance and extend sensor performance and lifetime. However,such materials can also provide advantages in the long term too (e.g.greater than 14 days). Particularly, the in vivo portion of the sensor(the portion of the sensor that is implanted into the host's tissue) isencased (partially or fully) in a porous material. The porous materialcan be wrapped around the sensor (for example, by wrapping the porousmaterial around the sensor or by inserting the sensor into a section ofporous material sized to receive the sensor). Alternately, the porousmaterial can be deposited on the sensor (for example, by electrospinningof a polymer directly thereon). In yet other alternative embodiments,the sensor is inserted into a selected section of porous biomaterial.Other methods for surrounding the in vivo portion of the sensor with aporous material can also be used as is appreciated by one skilled in theart.

The porous material surrounding the sensor advantageously slows orreduces cellular migration to the sensor and associated degradation thatwould otherwise be caused by cellular invasion if the sensor weredirectly exposed to the in vivo environment. Namely, the porous materialprovides a barrier that makes the migration of cells towards the sensormore tortuous and therefore slower (providing short term advantages). Itis believed that this reduces or slows the sensitivity loss normallyobserved in a short-term sensor over time.

In an embodiment wherein the porous material is a high oxygen solubilitymaterial, such as porous silicone, the high oxygen solubility porousmaterial surrounds some of or the entire in vivo portion 42 of thesensor. High oxygen solubility materials are materials that dynamicallyretain a high availability of oxygen that can be used to compensate forthe local oxygen deficit during times of transient ischemia (e.g.silicone and fluorocarbons). It is believed that some signal noisenormally seen by a conventional sensor can be attributed to an oxygendeficit. In one exemplary embodiment, porous silicone surrounds thesensor and thereby effectively increases the concentration of oxygenlocal (proximal) to the sensor. Thus, an increase in oxygen availabilityproximal to the sensor as achieved by this embodiment ensures that anexcess of oxygen over glucose is provided to the sensor; therebyreducing the likelihood of oxygen limited reactions therein.Accordingly, by providing a high oxygen solubility material (e.g. poroussilicone) surrounding the in vivo portion of the sensor, it is believedthat increased oxygen availability, reduced signal noise, longevity, andultimately enhanced sensor performance can be achieved.

Bioactive Agents

In some alternative embodiments, a bioactive agent is incorporated intothe above described porous material and/or membrane system, such as isdescribed in U.S. Patent Publication No. US-2005-0031689-A1, whichdiffuses out into the environment adjacent to the sensing region.Additionally or alternately, a bioactive agent can be administeredlocally at the exit-site or implantation-site. Suitable bioactive agentsare those that modify the host's tissue response to the sensor, forexample anti-inflammatory agents, anti-infective agents, anesthetics,inflammatory agents, growth factors, immunosuppressive agents,antiplatelet agents, anti-coagulants, anti-proliferates, ACE inhibitors,cytotoxic agents, anti-barrier cell compounds, vascularization-inducingcompounds, anti-sense molecules, or mixtures thereof, such as aredescribed in more detail in U.S. Patent Publication No.US-2005-0031689-A1.

In embodiments wherein the porous material is designed to enhanceshort-term (e.g., between about 1 and 14 days) lifetime or performanceof the sensor, a suitable bioactive agent can be chosen to ensure thattissue ingrowth does not substantially occur within the pores of theporous material. Namely, by providing a tissue modifying bioactiveagent, such as an anti-inflammatory agent (for example, Dexamethasone),substantially tissue ingrowth can be inhibited, at least in the shortterm, in order to maintain sufficient glucose transport through thepores of the porous material to maintain a stable sensitivity.

In embodiments wherein the porous material is designed to enhancelong-term (e.g. between about a day to a year or more) lifetime orperformance of the sensor, a suitable bioactive agent, such as avascularization-inducing compound or anti-barrier cell compound, can bechosen to encourage tissue ingrowth without barrier cell formation.

In some alternative embodiments, the in vivo portion of the sensor isdesigned with porosity therethrough, for example, a design wherein thesensor wires are configured in a mesh, loose helix configuration(namely, with spaces between the wires), or with micro-fabricated holestherethrough. Porosity within the sensor modifies the host's tissueresponse to the sensor, because tissue ingrowth into and/or through thein vivo portion of the sensor increases stability of the sensor and/orimproves host acceptance of the sensor, thereby extending the lifetimeof the sensor in vivo.

In some alternative embodiments, the sensor is manufactured partially orwholly using a continuous reel-to-reel process, wherein one or moremanufacturing steps are automated. In such embodiments, a manufacturingprocess can be provided substantially without the need for manualmounting and fixing steps and substantially without the need humaninteraction. A process can be utilized wherein a plurality of sensors ofthe preferred embodiments, including the electrodes, insulator, andmembrane system, are continuously manufactured in a semi-automated orautomated process.

In one embodiment, a plurality of twisted pairs is continuously formedinto a coil, wherein a working electrode is coated with an insulatormaterial around which a plurality of reference electrodes is wound. Theplurality of twisted pairs are preferably indexed and subsequently movedfrom one station to the next whereby the membrane system is seriallydeposited according to the preferred embodiments. Preferably, the coilis continuous and remains as such during the entire sensor fabricationprocess, including winding of the electrodes, insulator application, andmembrane coating processes. After drying of the membrane system, eachindividual sensor is cut from the continuous coil.

A continuous reel-to-reel process for manufacturing the sensoreliminates possible sensor damage due to handling by eliminatinghandling steps, and provides faster manufacturing due to faster troubleshooting by isolation when a product fails. Additionally, a process runcan be facilitated because of elimination of steps that would otherwisebe required (e.g. steps in a manual manufacturing process). Finally,increased or improved product consistency due to consistent processeswithin a controlled environment can be achieved in a machine or robotdriven operation.

In one alternative embodiment, a continuous manufacturing process iscontemplated that utilizes physical vapor deposition in a vacuum to formthe sensor. Physical vapor deposition can be used to coat one or moreinsulating layers onto the electrodes, and further can be used todeposit the membrane system thereon. While not wishing to be bound bytheory, it is believed that by implementing physical vapor deposition toform some portions or the entire sensor of the preferred embodiments,simplified manufacturing, consistent deposition, and overall increasedreproducibility can be achieved.

Applicator

FIG. 6 is an exploded side view of an applicator, showing the componentsthat enable sensor and needle insertion. In this embodiment, theapplicator 12 includes an applicator body 18 that aides in aligning andguiding the applicator components. Preferably, the applicator body 18includes an applicator body base 60 that matingly engages the mountingunit 14 and an applicator body cap 62 that enables appropriaterelationships (for example, stops) between the applicator components.

The guide tube subassembly 20 includes a guide tube carrier 64 and aguide tube 66. In some embodiments, the guide tube is a cannula. Theguide tube carrier 64 slides along the applicator body 18 and maintainsthe appropriate relative position of the guide tube 66 during insertionand subsequent retraction. For example, prior to and during insertion ofthe sensor, the guide tube 66 extends through the contact subassembly 26to maintain an opening that enables easy insertion of the needletherethrough (see FIGS. 7A to 7D). During retraction of the sensor, theguide tube subassembly 20 is pulled back, engaging with and causing theneedle and associated moving components to retract back into theapplicator 12 (See FIGS. 7C and 7D).

A needle subassembly 68 is provided that includes a needle carrier 70and needle 72. The needle carrier 70 cooperates with the otherapplicator components and carries the needle 72 between its extended andretracted positions. The needle can be of any appropriate size that canencompass the sensor 32 and aid in its insertion into the host.Preferred sizes include from about 32 gauge or less to about 18 gauge ormore, more preferably from about 28 gauge to about 25 gauge, to providea comfortable insertion for the host. Referring to the inner diameter ofthe needle, approximately 0.006 inches to approximately 0.023 inches ispreferable, and 0.013 inches is most preferable. The needle carrier 70is configured to engage with the guide tube carrier 64, while the needle72 is configured to slidably nest within the guide tube 66, which allowsfor easy guided insertion (and retraction) of the needle through thecontact subassembly 26.

A push rod subassembly 74 is provided that includes a push rod carrier76 and a push rod 78. The push rod carrier 76 cooperates with otherapplicator components to ensure that the sensor is properly insertedinto the host's skin, namely the push rod carrier 76 carries the pushrod 78 between its extended and retracted positions. In this embodiment,the push rod 78 is configured to slidably nest within the needle 72,which allows for the sensor 32 to be pushed (released) from the needle72 upon retraction of the needle, which is described in more detail withreference to FIGS. 7A through 7D. In some embodiments, a slight bend orserpentine shape is designed into or allowed in the sensor in order tomaintain the sensor within the needle by interference. While not wishingto be bound by theory, it is believed that a slight friction fit of thesensor within the needle minimizes motion of the sensor duringwithdrawal of the needle and maintains the sensor within the needleprior to withdrawal of the needle.

A plunger subassembly 22 is provided that includes a plunger 80 andplunger cap 82. The plunger subassembly 22 cooperates with otherapplicators components to ensure proper insertion and subsequentretraction of the applicator components. In this embodiment, the plunger80 is configured to engage with the push rod to ensure the sensorremains extended (namely, in the host) during retraction, such as isdescribed in more detail with reference to FIG. 7C.

Sensor Insertion

FIGS. 7A through 7D are schematic side cross-sectional views thatillustrate the applicator components and their cooperating relationshipsat various stages of sensor insertion. FIG. 7A illustrates the needleand sensor loaded prior to sensor insertion. FIG. 7B illustrates theneedle and sensor after sensor insertion. FIG. 7C illustrates the sensorand needle during needle retraction. FIG. 7D illustrates the sensorremaining within the contact subassembly after needle retraction.Although the embodiments described herein suggest manual insertionand/or retraction of the various components, automation of one or moreof the stages can also be employed. For example, spring-loadedmechanisms that can be triggered to automatically insert and/or retractthe sensor, needle, or other cooperative applicator components can beimplemented.

Referring to FIG. 7A, the sensor 32 is shown disposed within the needle72, which is disposed within the guide tube 66. In this embodiment, theguide tube 66 is provided to maintain an opening within the contactsubassembly 26 and/or contacts 28 to provide minimal friction betweenthe needle 72 and the contact subassembly 26 and/or contacts 28 duringinsertion and retraction of the needle 72. However, the guide tube is anoptional component, which can be advantageous in some embodimentswherein the contact subassembly 26 and/or the contacts 28 are formedfrom an elastomer or other material with a relatively high frictioncoefficient, and which can be omitted in other embodiments wherein thecontact subassembly 26 and or the contacts 28 are formed from a materialwith a relatively low friction coefficient (for example, hard plastic ormetal). A guide tube, or the like, can be preferred in embodimentswherein the contact subassembly 26 and/or the contacts 28 are formedfrom a material designed to frictionally hold the sensor 32 (see FIG.7D), for example, by the relaxing characteristics of an elastomer, orthe like. In these embodiments, the guide tube is provided to easeinsertion of the needle through the contacts, while allowing for africtional hold of the contacts on the sensor 32 upon subsequent needleretraction. Stabilization of the sensor in or on the contacts 28 isdescribed in more detail with reference to FIG. 7D and following.Although FIG. 7A illustrates the needle and sensor inserted into thecontacts subassembly as the initial loaded configuration, alternativeembodiments contemplate a step of loading the needle through the guidetube 66 and/or contacts 28 prior to sensor insertion.

Referring to FIG. 7B, the sensor 32 and needle 72 are shown in anextended position. In this stage, the pushrod 78 has been forced to aforward position, for example by pushing on the plunger shown in FIG. 6,or the like. The plunger 22 (FIG. 6) is designed to cooperate with otherof the applicator components to ensure that sensor 32 and the needle 72extend together to a forward position (as shown); namely, the push rod78 is designed to cooperate with other of the applicator components toensure that the sensor 32 maintains the forward position simultaneouslywithin the needle 72.

Referring to FIG. 7C, the needle 72 is shown during the retractionprocess. In this stage, the push rod 78 is held in its extended(forward) position in order to maintain the sensor 32 in its extended(forward) position until the needle 72 has substantially fully retractedfrom the contacts 28. Simultaneously, the cooperating applicatorcomponents retract the needle 72 and guide tube 66 backward by a pullingmotion (manual or automated) thereon. In preferred embodiments, theguide tube carrier 64 (FIG. 6) engages with cooperating applicatorcomponents such that a backward (retraction) motion applied to the guidetube carrier retracts the needle 72 and guide tube 66, without(initially) retracting the push rod 78. In an alternative embodiment,the push rod 78 can be omitted and the sensor 32 held it its forwardposition by a cam, elastomer, or the like, which is in contact with aportion of the sensor while the needle moves over another portion of thesensor. One or more slots can be cut in the needle to maintain contactwith the sensor during needle retraction.

Referring to FIG. 7D, the needle 72, guide tube 66, and push rod 78 areall retracted from contact subassembly 26, leaving the sensor 32disposed therein. The cooperating applicator components are designedsuch that when the needle 72 has substantially cleared from the contacts28 and/or contact subassembly 26, the push rod 78 is retracted alongwith the needle 72 and guide tube 66. The applicator 12 can then bereleased (manually or automatically) from the contacts 28, such as isdescribed in more detail elsewhere herein, for example with reference toFIGS. 8D and 9A.

The preferred embodiments are generally designed with elastomericcontacts to ensure a retention force that retains the sensor 32 withinthe mounting unit 14 and to ensure stable electrical connection of thesensor 32 and its associated contacts 28. Although the illustratedembodiments and associated text describe the sensor 32 extending throughthe contacts 28 to form a friction fit therein, a variety ofalternatives are contemplated. In one alternative embodiment, the sensoris configured to be disposed adjacent to the contacts (rather thanbetween the contacts). The contacts can be constructed in a variety ofknown configurations, for example, metallic contacts, cantileveredfingers, pogo pins, or the like, which are configured to press againstthe sensor after needle retraction.

The illustrated embodiments are designed with coaxial contacts 28;namely, the contacts 28 are configured to contact the working andreference electrodes 44, 46 axially along the distal portion 42 of thesensor 32 (see FIG. 5A). As shown in FIG. 5A, the working electrode 44extends farther than the reference electrode 46, which allows coaxialconnection of the electrodes 44, 46 with the contacts 28 at locationsspaced along the distal portion of the sensor (see also FIGS. 9B and10B). Although the illustrated embodiments employ a coaxial design,other designs are contemplated within the scope of the preferredembodiments. For example, the reference electrode can be positionedsubstantially adjacent to (but spaced apart from) the working electrodeat the distal portion of the sensor. In this way, the contacts 28 can bedesigned side-by-side rather than co-axially along the axis of thesensor.

FIG. 8A is a perspective view of an applicator and mounting unit in oneembodiment including a safety latch mechanism 84. The safety latchmechanism 84 is configured to lock the plunger subassembly 22 in astationary position such that it cannot be accidentally pushed prior torelease of the safety latch mechanism. In this embodiment, the sensorsystem 10 is preferably packaged (e.g. shipped) in this lockedconfiguration, wherein the safety latch mechanism 84 holds the plungersubassembly 22 in its extended position, such that the sensor 32 cannotbe prematurely inserted (e.g. accidentally released). The safety latchmechanism 84 is configured such that a pulling force shown in thedirection of the arrow (see FIG. 8A) releases the lock of the safetylatch mechanism on the plunger subassembly, thereby allowing sensorinsertion. Although one safety latch mechanism that locks the plungersubassembly is illustrated and described herein, a variety of safetylatch mechanism configurations that lock the sensor to prevent it fromprematurely releasing (i.e., that lock the sensor prior to release ofthe safety latch mechanism) are contemplated, as can be appreciated byone skilled in the art, and fall within the scope of the preferredembodiments.

FIG. 8A additionally illustrates a force-locking mechanism 86 includedin certain alternative embodiments of the sensor system, wherein theforce-locking mechanism 86 is configured to ensure a proper mate betweenthe electronics unit 16 and the mounting unit 14 (see FIG. 12A, forexample). In embodiments wherein a seal is formed between the mountingunit and the electronics unit, as described in more detail elsewhereherein, an appropriate force may be required to ensure a seal hassufficiently formed therebetween; in some circumstances, it can beadvantageous to ensure the electronics unit has been properly mated(e.g. snap-fit or sealingly mated) to the mounting unit. Accordingly,upon release of the applicator 12 from the mounting unit 14 (aftersensor insertion), and after insertion of the electronics unit 16 intothe mounting unit 14, the force-locking mechanism 86 allows the user toensure a proper mate and/or seal therebetween. In practice, a userpivots the force-locking mechanism such that it provides force on theelectronics unit 16 by pulling up on the circular tab illustrated inFIG. 8A. Although one system and one method for providing a secureand/or sealing fit between the electronics unit and the mounting unitare illustrated, various other force-locking mechanisms can be employedthat utilize a variety of systems and methods for providing a secureand/or sealing fit between the electronics unit and the mounting unit(housing).

FIGS. 8B to 8D are side views of an applicator and mounting unit in oneembodiment, showing various stages of sensor insertion. FIG. 8B is aside view of the applicator matingly engaged to the mounting unit priorto sensor insertion. FIG. 8C is a side view of the mounting unit andapplicator after the plunger subassembly has been pushed, extending theneedle and sensor from the mounting unit (namely, through the host'sskin). FIG. 8D is a side view of the mounting unit and applicator afterthe guide tube subassembly has been retracted, retracting the needleback into the applicator. Although the drawings and associated textillustrate and describe embodiments wherein the applicator is designedfor manual insertion and/or retraction, automated insertion and/orretraction of the sensor/needle, for example, using spring-loadedcomponents, can alternatively be employed.

The preferred embodiments advantageously provide a system and method foreasy insertion of the sensor and subsequent retraction of the needle ina single push-pull motion. Because of the mechanical latching system ofthe applicator, the user provides a continuous force on the plunger cap82 and guide tube carrier 64 that inserts and retracts the needle in acontinuous motion. When a user grips the applicator, his or her fingersgrasp the guide tube carrier 64 while his or her thumb (or anotherfinger) is positioned on the plunger cap 82. The user squeezes his orher fingers and thumb together continuously, which causes the needle toinsert (as the plunger slides forward) and subsequently retract (as theguide tube carrier slides backward) due to the system of latches locatedwithin the applicator (FIGS. 6 to 8) without any necessary change ofgrip or force, leaving the sensor implanted in the host. In someembodiments, a continuous torque, when the applicator components areconfigured to rotatingly engage one another, can replace the continuousforce. Some prior art sensors, in contrast to the sensors of thepreferred embodiments, suffer from complex, multi-step, ormulti-component insertion and retraction steps to insert and remove theneedle from the sensor system.

FIG. 8B shows the mounting unit and applicator in the ready position.The sensor system can be shipped in this configuration, or the user canbe instructed to mate the applicator 12 with the mounting unit 14 priorto sensor insertion. The insertion angle α is preferably fixed by themating engagement of the applicator 12. In the illustrated embodiment,the insertion angle α is fixed in the applicator 12 by the angle of theapplicator body base 60 with the shaft of the applicator body 18.However, a variety of systems and methods of ensuring proper placementcan be implemented. Proper placement ensures that at least a portion ofthe sensor 32 extends below the dermis of the host upon insertion. Inalternative embodiments, the sensor system 10 is designed with a varietyof adjustable insertion angles. A variety of insertion angles can beadvantageous to accommodate a variety of insertion locations and/orindividual dermis configurations (for example, thickness of the dermis).In preferred embodiments, the insertion angle α is from about 0 to about90 degrees, more preferably from about 30 to about 60 degrees, and evenmore preferably about 45 degrees.

In practice, the mounting unit is placed at an appropriate location onthe host's skin, for example, the skin of the arm, thigh, or abdomen.Thus, removing the backing layer 9 from the adhesive pad 8 and pressingthe base portion of the mounting unit on the skin adheres the mountingunit to the host's skin.

FIG. 8C shows the mounting unit and applicator after the needle 72 hasbeen extended from the mounting unit 14 (namely, inserted into the host)by pushing the push rod subassembly 22 into the applicator 12. In thisposition, the sensor 32 is disposed within the needle 72 (namely, inposition within the host), and held by the cooperating applicatorcomponents. In alternative embodiments, the mounting unit and/orapplicator can be configured with the needle/sensor initially extended.In this way, the mechanical design can be simplified and theplunger-assisted insertion step can be eliminated or modified. Theneedle can be simply inserted by a manual force to puncture the host'sskin, and only one (pulling) step is required on the applicator, whichremoves the needle from the host's skin.

FIG. 8D shows the mounting unit and applicator after the needle 72 hasbeen retracted into the applicator 12, exposing the sensor 32 to thehost's tissue. During needle retraction, the push rod subassemblymaintains the sensor in its extended position (namely, within the host).In preferred embodiments, retraction of the needle irreversibly locksthe needle within the applicator so that it cannot be accidentallyand/or intentionally released, reinserted, or reused. The applicator ispreferably configured as a disposable device to reduce or eliminate apossibility of exposure of the needle after insertion into the host.However a reusable or reloadable applicator is also contemplated in somealternative embodiments. After needle retraction, the applicator 12 canbe released from the mounting unit, for example, by pressing the releaselatch(es) 30, and the applicator disposed of appropriately. Inalternative embodiments, other mating and release configurations can beimplemented between the mounting unit and the applicator, or theapplicator can automatically release from the mounting unit after sensorinsertion and subsequent needle retraction. In one alternativeembodiment, a retention hold (e.g., ball and detent configuration) holdsand releases the electronics unit (or applicator).

In one alternative embodiment, the mounting unit is configured toreleasably mate with the applicator and electronics unit in a mannersuch that when the applicator is releasably mated to the mounting unit(e.g., after sensor insertion), the electronics unit is configured toslide into the mounting unit, thereby triggering release of theapplicator and simultaneous mating of the electronics unit to themounting unit. Cooperating mechanical components, for example, slidingball and detent type configurations, can be used to accomplish thesimultaneous mating of electronics unit and release of the applicator.

FIGS. 8E to 8G are perspective views of a sensor system 310 of analternative embodiment, including an applicator 312, electronics unit316, and mounting unit 314, showing various stages of applicator releaseand/or electronic unit mating. FIG. 8E is a perspective view of theapplicator matingly engaged to the mounting unit after sensor insertion.FIG. 8F is a perspective view of the mounting unit and applicatormatingly engaged while the electronics unit is slidingly inserted intothe mounting unit. FIG. 8G is a perspective view of the electronics unitmatingly engaged with the mounting unit after the applicator has beenreleased.

In general, the sensor system 310 comprises a sensor adapted fortranscutaneous insertion into a host's skin; a housing 314 adapted forplacement adjacent to the host's skin; an electronics unit 316releasably attachable to the housing; and an applicator 312 configuredto insert the sensor through the housing 314 and into the skin of thehost, wherein the applicator 312 is adapted to releasably mate with thehousing 314, and wherein the system 310 is configured to release theapplicator 312 from the housing when the electronics unit 316 isattached to the housing 314.

FIG. 8E shows the sensor system 310 after the sensor has been insertedand prior to release of the applicator 312. In this embodiment, theelectronics unit 316 is designed to slide into the mounting unit 314.Preferably, the electronics unit 316 is configured and arranged to slideinto the mounting unit 314 in only one orientation. In the illustratedembodiment, the insertion end is slightly tapered and dovetailed inorder to guide insertion of the electronics unit 316 into the housing314; however other self-alignment configurations are possible. In thisway, the electronics unit 316 self-aligns and orients the electronicsunit 316 in the housing, ensuring a proper fit and a secure electronicconnection with the sensor.

FIG. 8F shows the sensor system 310 after the electronics unit 316 hasbeen inserted therein. Preferably, the electronic unit 316 slide-fitsinto the mounting unit. In some embodiments, the sensor system 310 canbe designed to allow the electronics unit 316 to be attached to themounting unit 314 (i.e., operably connected to the sensor) before thesensor system 310 is affixed to the host. Advantageously, this designprovides mechanical stability for the sensor during transmitterinsertion.

FIG. 8G shows the sensor system 310 upon release of the applicator 312from the mounting unit 314 and electronics unit 316. In this embodiment,the sensor system 310 is configured such that mating the electronicsunit to the mounting unit triggers the release of the applicator 312from the mounting unit 314.

Thus, the above described sensor system 310, also referred to as theslide-in system, allows for self-alignment of the electronics unit,creates an improved seal around the contacts due to greater holdingforce, provides mechanical stability for the sensor during insertion ofthe electronics unit, and causes automatic release of the applicator andsimultaneous lock of the electronics unit into the mounting unit.

Although the overall design of the sensor system 10 results in aminiaturized volume as compared to numerous conventional devices, asdescribed in more detail below; the sensor system 310 further enables areduction in volume, as compared to, for example, the sensor system 10described above.

FIGS. 8H and 8I are comparative top views of the sensor system shown inthe alternative embodiment illustrated in FIGS. 8E to 8G and compared tothe embodiments illustrated elsewhere (see FIGS. 1 to 3 and 10 to 12,for example). Namely, the alternative embodiment described withreference to FIGS. 8E to 8G further enables reduced size (e.g. mass,volume, and the like) of the device as compared to certain otherdevices. It has been discovered that the size (including volume and/orsurface area) of the device can affect the function of the device. Forexample, motion of the mounting unit/electronics unit caused by externalinfluences (e.g. bumping or other movement on the skin) is translated tothe sensor in vivo causing motion artifact (e.g. an effect on thesignal, or the like). Accordingly, by enabling a reduction of size, amore stable signal with overall improved patient comfort can beachieved.

Accordingly, slide-in system 310 described herein, including the systemsand methods for inserting the sensor and connecting the electronics unitto the mounting unit, enables the mounting unit 316/electronics unit 314subassembly to have a volume of less than about 10 cm³, more preferablyless than about 8 cm³, and even more preferably less than about 6 cm³, 5cm³, or 4 cm³ or less. In general, the mounting unit 316/electronicsunit 314 subassembly comprises a first major surface and a second majorsurface opposite the first major surface. The first and second majorsurfaces together preferably account for at least about 50% of thesurface area of the device; the first and second major surfaces eachdefine a surface area, wherein the surface area of each major surface isless than or equal to about 10 cm², preferably less than or equal toabout 8 cm², and more preferably less than or equal to about 6.5 cm², 6cm², 5.5 cm², 5 cm², 4.5 cm², or 4 cm² or less. Typically, the mountingunit 316/electronics unit 314 subassembly has a length 320 of less thanabout 40 mm by a width 322 of less than about 20 mm and a thickness ofless than about 10 mm, and more preferably a length 320 less than orequal to about 35 mm by a width 322 less than or equal to about 18 mm bya thickness of less than or equal to about 9 mm.

In some embodiments, the mounting unit 14/electronics unit 16 assemblyhas the following dimensional properties: preferably a length of about 6cm or less, more preferably about 5 cm or less, more preferably stillabout 4.6 cm or less, even more preferably 4 cm or less, and mostpreferably about 3 cm or less; preferably a width of about 5 cm or less,more preferably about 4 cm or less, even more preferably 3 cm or less,even more preferably still about 2 cm or less, and most preferably about1.5 cm or less; and/or preferably a thickness of about 2 cm or less,more preferably about 1.3 cm or less, more preferably still about 1 cmor less, even more preferably still about 0.7 cm or less, and mostpreferably about 0.5 cm or less. The mounting unit 14/electronics unit16 assembly preferably has a volume of about 20 cm³ or less, morepreferably about 10 cm³ or less, more preferably still about 5 cm³ orless, and most preferably about 3 cm³ or less; and preferably weighs 12g or less, more preferably about 9 g or less, and most preferably about6 g or less, although in some embodiments the electronics unit may weighmore than about 12 g, e.g., up to about 25 g, 45 g, or 90 g.

In some embodiments, the sensor 32 exits the base of the mounting unit14 at a location distant from an edge of the base. In some embodiments,the sensor 32 exits the base of the mounting unit 14 at a locationsubstantially closer to the center than the edges thereof. While notwishing to be bound by theory, it is believed that by providing an exitport for the sensor 32 located away from the edges, the sensor 32 can beprotected from motion between the body and the mounting unit, snaggingof the sensor by an external source, and/or environmental contaminants(e.g. microorganisms) that can migrate under the edges of the mountingunit. In some embodiments, the sensor exits the mounting unit away froman outer edge of the device. FIG. 23 shows transcutaneous glucose sensordata and corresponding blood glucose values obtained over approximatelyseven days in a human, wherein the transcutaneous glucose sensor datawas configured with an exit port situated at a location substantiallycloser to the center than the edges of the base.

In some alternative embodiments, however, the sensor exits the mountingunit 14 at an edge or near an edge of the device. In some embodiments,the mounting unit is configured such that the exit port (location) ofthe sensor is adjustable; thus, in embodiments wherein the depth of thesensor insertion is adjustable, six-degrees of freedom can thereby beprovided.

Extensible Adhesive Pad

In certain embodiments, an adhesive pad is used with the sensor system.A variety of design parameters are desirable when choosing an adhesivepad for the mounting unit. For example: 1) the adhesive pad can bestrong enough to maintain full contact at all times and during allmovements (devices that release even slightly from the skin have agreater risk of contamination and infection), 2) the adhesive pad can bewaterproof or water permeable such that the host can wear the deviceeven while heavily perspiring, showering, or even swimming in somecases, 3) the adhesive pad can be flexible enough to withstand linearand rotational forces due to host movements, 4) the adhesive pad can becomfortable for the host, 5) the adhesive pad can be easily releasableto minimize host pain, 6) and/or the adhesive pad can be easilyreleasable so as to protect the sensor during release. Unfortunately,these design parameters are difficult to simultaneously satisfy usingknown adhesive pads, for example, strong medical adhesive pads areavailable but are usually non-precise (for example, requiringsignificant “ripping” force during release) and can be painful duringrelease due to the strength of their adhesion.

Therefore, the preferred embodiments provide an adhesive pad 8′ formounting the mounting unit onto the host, including a sufficientlystrong medical adhesive pad that satisfies one or more strength andflexibility requirements described above, and further provides a foreasy, precise and pain-free release from the host's skin. FIG. 9A is aside view of the sensor assembly, illustrating the sensor implanted intothe host with mounting unit adhered to the host's skin via an adhesivepad in one embodiment. Namely, the adhesive pad 8′ is formed from anextensible material that can be removed easily from the host's skin bystretching it lengthwise in a direction substantially parallel to (or upto about 35 degrees from) the plane of the skin. It is believed thatthis easy, precise, and painless removal is a function of both the highextensibility and easy stretchability of the adhesive pad.

In one embodiment, the extensible adhesive pad includes a polymeric foamlayer or is formed from adhesive pad foam. It is believed that theconformability and resiliency of foam aids in conformation to the skinand flexibility during movement of the skin. In another embodiment, astretchable solid adhesive pad, such as a rubber-based or anacrylate-based solid adhesive pad can be used. In another embodiment,the adhesive pad comprises a film, which can aid in increasing loadbearing strength and rupture strength of the adhesive pad

FIGS. 9B to 9C illustrate initial and continued release of the mountingunit from the host's skin by stretching the extensible adhesive pad inone embodiment. To release the device, the backing adhesive pad ispulled in a direction substantially parallel to (or up to about 35degrees from) the plane of the device. Simultaneously, the extensibleadhesive pad stretches and releases from the skin in a relatively easyand painless manner.

In one implementation, the mounting unit is bonded to the host's skinvia a single layer of extensible adhesive pad 8′, which is illustratedin FIGS. 9A to 9C. The extensible adhesive pad includes a substantiallynon-extensible pull-tab 52, which can include a light adhesive pad layerthat allows it to be held on the mounting unit 14 prior to release.Additionally, the adhesive pad can further include a substantiallynon-extensible holding tab 54, which remains attached to the mountingunit during release stretching to discourage complete and/oruncontrolled release of the mounting unit from the skin.

In one alternative implementation, the adhesive pad 8′ includestwo-sides, including the extensible adhesive pad and a backing adhesivepad (not shown). In this embodiment, the backing adhesive pad is bondedto the mounting unit's back surface 25 while the extensible adhesive pad8′ is bonded to the host's skin. Both adhesive pads provide sufficientstrength, flexibility, and waterproof or water permeable characteristicsappropriate for their respective surface adhesion. In some embodiments,the backing and extensible adhesive pads are particularly designed withan optimized bond for their respective bonding surfaces (namely, themounting unit and the skin).

In another alternative implementation, the adhesive pad 8′ includes adouble-sided extensible adhesive pad surrounding a middle layer orbacking layer (not shown). The backing layer can comprise a conventionalbacking film or can be formed from foam to enhance comfort,conformability, and flexibility. Preferably, each side of thedouble-sided adhesive pad is respectively designed for appropriatebonding surface (namely, the mounting unit and skin). A variety ofalternative stretch-release configurations are possible. Controlledrelease of one or both sides of the adhesive pad can be facilitated bythe relative lengths of each adhesive pad side, by incorporation of anon-adhesive pad zone, or the like.

FIGS. 10A and 10B are perspective and side cross-sectional views,respectively, of the mounting unit immediately following sensorinsertion and release of the applicator from the mounting unit. In oneembodiment, such as illustrated in FIGS. 10A and 10B, the contactsubassembly 26 is held in its insertion position, substantially at theinsertion angle α of the sensor. Maintaining the contact subassembly 26at the insertion angle α during insertion enables the sensor 32 to beeasily inserted straight through the contact subassembly 26. The contactsubassembly 26 further includes a hinge 38 that allows movement of thecontact subassembly 26 from an angled to a flat position. The term“hinge,” as used herein, is a broad term and is used in its ordinarysense, including, without limitation, a mechanism that allowsarticulation of two or more parts or portions of a device. The term isbroad enough to include a sliding hinge, for example, a ball and detenttype hinging mechanism.

Although the illustrated embodiments describe a fixed insertion angledesigned into the applicator, alternative embodiments can design theinsertion angle into other components of the system. For example, theinsertion angle can be designed into the attachment of the applicatorwith the mounting unit, or the like. In some alternative embodiments, avariety of adjustable insertion angles can be designed into the systemto provide for a variety of host dermis configurations.

FIG. 10B illustrates the sensor 32 extending from the mounting unit 14by a preselected distance, which defines the depth of insertion of thesensor into the host. The dermal and subcutaneous make-up of animals andhumans is variable and a fixed depth of insertion may not be appropriatefor all implantations. Accordingly, in an alternative embodiment, thedistance that the sensor extends from the mounting unit is adjustable toaccommodate a variety of host body-types. For example, the applicator 12can be designed with a variety of adjustable settings, which control thedistance that the needle 72 (and therefore the sensor 32) extends uponsensor insertion. One skilled in the art appreciates a variety of meansand mechanisms can be employed to accommodate adjustable sensorinsertion depths, which are considered within the scope of the preferredembodiments. The preferred insertion depth is from about 0.1 mm or lessto about 2 cm or more, preferably from about 0.15, 0.2, 0.25, 0.3, 0.35,0.4, or 0.45 mm to about 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4,1.5, 1.6, 1.7, 1.8, or 1.9 cm.

FIGS. 11A and 11B are perspective and side cross-sectional views,respectively, of the mounting unit after articulating the contactsubassembly to its functional position (which is also referred to as aninserted, implanted, or sensing position). The hinge 38 enables thecontact subassembly 26 to tilt from its insertion position (FIG. 10) toits functional position (FIG. 11) by pressing downward on the contactsubassembly, for example. Certain embodiments provide this pivotalmovement via two separate pieces (the contact subassembly 26 and themounting unit 14 connected by a hinge, for example, a mechanical oradhesive pad joint or hinge. A variety of pivoting, articulating, and/orhinging mechanisms can be employed with the sensors of preferredembodiments. For example, the hinge can be formed as a part of thecontact subassembly 26. The contact subassembly can be formed from aflexible piece of material (such as silicone, urethane rubber, or otherflexible or elastomeric material), wherein the material is sufficientlyflexible to enable bending or hinging of the contact subassembly from anangle appropriate for insertion (FIGS. 10A and 10B) to a lowerfunctional configuration (FIGS. 11A and 11B).

The relative pivotal movement of the contact subassembly isadvantageous, for example, for enabling the design of a low profiledevice while providing support for an appropriate needle insertionangle. In its insertion position, the sensor system is designed for easysensor insertion while forming a stable electrical connection with theassociated contacts 28. In its functional position, the sensor systemmaintains a low profile for convenience, comfort, and discreetnessduring use. Thus, the sensor systems of preferred embodiments areadvantageously designed with a hinging configuration to provide anoptimum guided insertion angle while maintaining a low profile deviceduring sensor use.

In some embodiments, a shock-absorbing member or feature is incorporatedinto the design of the sensor and configured to absorb movement of thein vivo and/or ex vivo portion of the sensor. Conventional analytesensors can suffer from motion-related artifact associated with hostmovement when the host is using the device. For example, when atranscutaneous analyte sensor is inserted into the host, variousmovements on the sensor (for example, relative movement between the invivo portion and the ex vivo portion and/or movement within the host)create stresses on the device and can produce noise in the sensorsignal. Accordingly in some embodiments, a shock-absorbing member islocated on the sensor/mounting unit in a location that absorbs stressesassociated with the above-described movement.

In the preferred embodiments, the sensor 32 bends from a substantiallystraight to substantially bent configuration upon pivoting of thecontact subassembly from the insertion to functional position. Thesubstantially straight sensor configuration during insertionadvantageously provides ease of sensor insertion, while the substantialbend in the sensor in its functional position advantageously providesstability on the proximal end of the sensor with flexibility/mobility onthe distal end of the sensor. Additionally, motion within the mountingunit (e.g. caused by external forces to the mounting unit, movement ofthe skin, and the like) does not substantially translate to the in vivoportion of the sensor. Namely, the bend formed within the sensor 32functions to break column strength, causing flexion that effectivelyabsorbs movements on the sensor during use. Additionally, the sensor canbe designed with a length such that when the contact subassembly 26 ispivoted to its functional position (FIG. 10B), the sensor pushes forwardand flexes, allowing it to absorb motion between the in vivo and ex vivoportions of the sensor. It is believed that both of the above advantagesminimize motion artifact on the sensor signal and/or minimize damage tothe sensor caused by movement, both of which (motion artifact anddamage) have been observed in conventional transcutaneous sensors.

In some alternative embodiments, the shock-absorbing member can be anexpanding and contracting member, such as a spring, accordion,telescoping, or bellows-type device. In general, the shock absorbingmember can be located such that relative movement between the sensor,the mounting unit, and the host is absorbed without (or minimally)affecting the connection of the sensor to the mounting unit and/or thesensor stability within the implantation site; for example, theshock-absorbing member can be formed as a part of or connected to thesensor 32.

FIGS. 12A to 12C are perspective and side views of a sensor systemincluding the mounting unit 14 and electronics unit 16 attached thereto.After sensor insertion, the transcutaneous analyte sensor system 10measures a concentration of an analyte or a substance indicative of theconcentration or presence of the analyte as described above. Althoughthe examples are directed to a glucose sensor, the analyte sensor can bea sensor capable of determining the level of any suitable analyte in thebody, for example, oxygen, lactase, insulin, hormones, cholesterol,medicaments, viruses, or the like. Once the electronics unit 16 isconnected to the mounting unit 14, the sensor 32 is able to measurelevels of the analyte in the host.

Detachable connection between the mounting unit 14 and electronics unit16 provides improved manufacturability, namely, the relativelyinexpensive mounting unit 14 can be disposed of when replacing thesensor system after its usable life, while the relatively more expensiveelectronics unit 16 can be reusable with multiple sensor systems. Incertain embodiments, the electronics unit 16 is configured withprogramming, for example, initialization, calibration reset, failuretesting, or the like, each time it is initially inserted into the cavityand/or each time it initially communicates with the sensor 32. However,an integral (non-detachable) electronics unit can be configured as isappreciated by one skilled in the art.

Referring to the mechanical fit between the mounting unit 14 and theelectronics unit 16 (and/or applicator 12), a variety of mechanicaljoints are contemplated, for example, snap fit, interference fit, orslide fit. In the illustrated embodiment of FIGS. 12A to 12C, tabs 120are provided on the mounting unit 14 and/or electronics unit 16 thatenable a secure connection therebetween. The tabs 120 of the illustratedembodiment can improve ease of mechanical connection by providingalignment of the mounting unit and electronics unit and additional rigidsupport for force and counter force by the user (e.g., fingers) duringconnection. However, other configurations with or without guiding tabsare contemplated, such as illustrated in FIGS. 10 and 11, for example.

In some circumstances, a drift of the sensor signal can causeinaccuracies in sensor performance and/or require re-calibration of thesensor. Accordingly, it can be advantageous to provide a sealant,whereby moisture (e.g. water and water vapor) cannot substantiallypenetrate to the sensor and its connection to the electrical contacts.The sealant described herein can be used alone or in combination withthe sealing member 36 described in more detail above, to seal the sensorfrom moisture in the external environment.

Preferably, the sealant fills in holes, crevices, or other void spacesbetween the mounting unit 14 and electronics unit 16 and/or around thesensor 32 within the mounting unit 32. For example, the sealant cansurround the sensor in the portion of the sensor 32 that extends throughthe contacts 28. Additionally, the sealant can be disposed within theadditional void spaces, for example a hole 122 that extends through thesealing member 36.

Preferably, the sealant comprises a water impermeable material orcompound, for example, oil, grease, or gel. In one exemplary embodiment,the sealant comprises petroleum jelly and is used to provide a moisturebarrier surrounding the sensor 32. In one experiment, petroleum jellywas liquefied by heating, after which a sensor 32 was immersed into theliquefied petroleum jelly to coat the outer surfaces thereof. The sensorwas then assembled into a housing and inserted into a host, during whichdeployment the sensor was inserted through the electrical contacts 28and the petroleum jelly conforming therebetween. Sensors incorporatingpetroleum jelly, such as described above, when compared to sensorswithout the petroleum jelly moisture barrier exhibited less or no signaldrift over time when studied in a humid or submersed environment. Whilenot wishing to be bound by theory, it is believed that incorporation ofa moisture barrier surrounding the sensor, especially between the sensorand its associated electrical contacts, reduces or eliminates theeffects of humidity on the sensor signal. The viscosity of grease oroil-based moisture barriers allows penetration into and through evensmall cracks or crevices within the sensor and mounting unit, displacingmoisture and thereby increasing the sealing properties thereof. U.S.Pat. Nos. 4,259,540 and 5,285,513 disclose materials suitable for use asa water impermeable material (sealant).

Referring to the electrical fit between the sensor 32 and theelectronics unit 16, contacts 28 (through which the sensor extends) areconfigured to electrically connect with mutually engaging contacts onthe electronics unit 16. A variety of configurations are contemplated;however, the mutually engaging contacts operatively connect upondetachable connection of the electronics unit 16 with the mounting unit14, and are substantially sealed from external moisture by sealingmember 36. Even with the sealing member, some circumstances can existwherein moisture can penetrate into the area surrounding the sensor 32and or contacts, for example, exposure to a humid or wet environment(e.g., caused by sweat, showering, or other environmental causes). Ithas been observed that exposure of the sensor to moisture can be a causeof baseline signal drift of the sensor over time. For example in aglucose sensor, the baseline is the component of a glucose sensor signalthat is not related to glucose (the amount of signal if no glucose ispresent), which is ideally constant over time. However, somecircumstances my exist wherein the baseline can fluctuate over time,also referred to as drift, which can be caused, for example, by changesin a host's metabolism, cellular migration surrounding the sensor,interfering species, humidity in the environment, and the like.

In some embodiments, the mounting unit is designed to provideventilation (e.g., a vent hole 124) between the exit-site and thesensor. In certain embodiments, a filter (not shown) is provided in thevent hole 124 that allows the passage of air, while preventingcontaminants from entering the vent hole 124 from the externalenvironment. While not wishing to be bound by theory, it is believedthat ventilation to the exit-site (or to the sensor 32) can reduce oreliminate trapped moisture or bacteria, which can otherwise increase thegrowth and/or lifetime of bacteria adjacent to the sensor.

In some alternative embodiments, a sealing material is provided, whichseals the needle and/or sensor from contamination of the externalenvironment during and after sensor insertion. For example, one problemencountered in conventional transcutaneous devices is infection of theexit-site of the wound. For example, bacteria or contaminants canmigrate from ex vivo, for example, any ex vivo portion of the device orthe ex vivo environment, through the exit-site of the needle/sensor, andinto the subcutaneous tissue, causing contamination and infection.Bacteria and/or contaminants can originate from handling of the device,exposed skin areas, and/or leakage from the mounting unit (external to)on the host. In many conventional transcutaneous devices, there existssome path of migration for bacteria and contaminants to the exit-site,which can become contaminated during sensor insertion or subsequenthandling or use of the device. Furthermore, in some embodiments of atranscutaneous analyte sensor, the insertion-aiding device (for example,needle) is an integral part of the mounting unit; namely, the devicestores the insertion device after insertion of the sensor, which isisolated from the exit-site (namely, point-of-entry of the sensor) afterinsertion.

Accordingly, these alternative embodiments provide a sealing material onthe mounting unit, interposed between the housing and the skin, whereinthe needle and/or sensor are adapted to extend through, and be sealedby, the sealing material. The sealing material is preferably formed froma flexible material that substantially seals around the needle/sensor.Appropriate flexible materials include malleable materials, elastomers,gels, greases, or the like (e.g. see U.S. Pat. Nos. 4,259,540 and5,285,513). However, not all embodiments include a sealing material, andin some embodiments a clearance hole or other space surrounding theneedle and/or sensor is preferred.

In one embodiment, the base 24 of the mounting unit 14 is formed from aflexible material, for example silicone, which by its elastomericproperties seals the needle and/or sensor at the exit port 126, such asis illustrated in FIGS. 11A and 11B. Thus, sealing material can beformed as a unitary or integral piece with the back surface 25 of themounting unit 14, or with an adhesive pad 8 on the back surface of themounting unit, however alternatively can be a separate part secured tothe device. In some embodiments, the sealing material can extend throughthe exit port 126 above or below the plane of the adhesive pad surface,or the exit port 126 can comprise a septum seal such as those used inthe medical storage and disposal industries (for example, silica gelsandwiched between upper and lower seal layers, such as layerscomprising chemically inert materials such as PTFE). A variety of knownseptum seals can be implemented into the exit port of the preferredembodiments described herein. Whether the sealing material is integralwith or a separate part attached to the mounting unit 14, the exit port126 is advantageously sealed so as to reduce or eliminate the migrationof bacteria or other contaminants to or from the exit-site of the woundand/or within the mounting unit.

During use, a host or caretaker positions the mounting unit at theappropriate location on or near the host's skin and prepares for sensorinsertion. During insertion, the needle aids in sensor insertion, afterwhich the needle is retracted into the mounting unit leaving the sensorin the subcutaneous tissue. In this embodiment, the exit-port 126includes a layer of sealing material, such as a silicone membrane, thatencloses the exit-port in a configuration that protects the exit-sitefrom contamination that can migrate from the mounting unit or spacingexternal to the exit-site. Thus, when the sensor 32 and/or needle 72extend through, for example, an aperture or a puncture in the sealingmaterial, to provide communication between the mounting unit andsubcutaneous space, a seal is formed therebetween. Elastomeric sealingmaterials can be advantageous in some embodiments because the elasticityprovides a conforming seal between the needle/sensor and the mountingunit and/or because the elasticity provides shock-absorbing qualitiesallowing relative movement between the device and the various layers ofthe host's tissue, for example.

In some alternative embodiments, the sealing material includes abioactive agent incorporated therein. Suitable bioactive agents includethose which are known to discourage or prevent bacteria and infection,for example, anti-inflammatory, antimicrobials, antibiotics, or thelike. It is believed that diffusion or presence of a bioactive agent canaid in prevention or elimination of bacteria adjacent to the exit-site.

In practice, after the sensor 32 has been inserted into the host'stissue, and an electrical connection formed by mating the electronicsunit 16 to the mounting unit 14, the sensor measures an analyteconcentration continuously or continually, for example, at an intervalof from about fractions of a second to about 10 minutes or more.

FIG. 13 is an exploded perspective view of one exemplary embodiment of acontinuous glucose sensor 1310A. In this embodiment, the sensor ispreferably wholly implanted into the subcutaneous tissue of a host, suchas described in U.S. Patent Publication No. US-2006-0015020-A1; U.S.Patent Publication No. US-2005-0245799-A1; U.S. Patent Publication No.US-2005-0192557-A1; U.S. Pat. No. 7,134,999; U.S. Patent Publication No.US-2005-0027463-A1; and U.S. Pat. No. 6,001,067, each of which isincorporated herein by reference in their entirety. In this exemplaryembodiment, a body 1320 and a sensing region 1321 house the electrodes1322 and sensor electronics (see FIG. 14). The three electrodes 1322 areoperably connected to the sensor electronics (see FIG. 14) and arecovered by a sensing membrane 1323 and a biointerface membrane 1324,which are attached by a clip 1325.

In one embodiment, the three electrodes 1322 include a platinum workingelectrode, a platinum counter electrode, and a silver/silver chloridereference electrode. The top ends of the electrodes are in contact withan electrolyte phase (not shown), which is a free-flowing fluid phasedisposed between the sensing membrane 1323 and the electrodes 1322. Thesensing membrane 1323 includes an enzyme, for example, glucose oxidase,and covers the electrolyte phase. The biointerface membrane 1324 coversthe sensing membrane 1323 and serves, at least in part, to protect thesensor 1310A from external forces that can result in environmentalstress cracking of the sensing membrane 1323. U.S. Pat. No. 7,192,450describes a biointerface membrane that can be used in conjunction withthe preferred embodiments, and is incorporated herein by reference inits entirety.

In one embodiment, the biointerface membrane 1324 generally includes acell disruptive domain most distal from the electrochemically reactivesurfaces and a cell impermeable domain less distal from theelectrochemically reactive surfaces than the cell disruptive domain. Thecell disruptive domain is preferably designed to support tissueingrowth, disrupt contractile forces typically found in a foreign bodyresponse, encourage vascularity within the membrane, and disrupt theformation of a barrier cell layer. The cell impermeable domain ispreferably resistant to cellular attachment, impermeable to cells, andcomposed of a biostable material.

In one embodiment, the sensing membrane 1323 generally provides one ormore of the following functions: 1) protection of the exposed electrodesurface from the biological environment, 2) diffusion resistance(limitation) of the analyte, 3) a catalyst for enabling an enzymaticreaction, 4) limitation or blocking of interfering species, and 5)hydrophilicity at the electrochemically reactive surfaces of the sensorinterface, such as described in U.S. Patent Publication No.2005-0245799-A1, which is incorporated herein by reference in itsentirety. Accordingly, the sensing membrane 1323 preferably includes aplurality of domains or layers, for example, an electrolyte domain, aninterference domain, an enzyme domain (for example, glucose oxidase), aresistance domain, and can additionally include an oxygen domain (notshown), and/or a bioprotective domain (not shown), such as described inmore detail herein and in the above-cited U.S. Patent Publication No.2005-0245799-A1. However, it is understood that a sensing membranemodified for other devices, for example, by including fewer oradditional domains is within the scope of the preferred embodiments.

In some embodiments, the domains of the biointerface and sensingmembranes are formed from materials such as silicone,polytetrafluoroethylene, polyethylene-co-tetrafluoroethylene,polyolefin, polyester, polycarbonate, biostable polytetrafluoroethylene,homopolymers, copolymers, terpolymers of polyurethanes, polypropylene(PP), polyvinylchloride (PVC), polyvinylidene fluoride (PVDF),polybutylene terephthalate (PBT), polymethylmethacrylate (PMMA),polyether ether ketone (PEEK), polyurethanes, cellulosic polymers,polysulfones and block copolymers thereof including, for example,di-block, tri-block, alternating, random and graft copolymers. U.S.Patent Publication No. 2005-0245799-A1, which is incorporated herein byreference in its entirety, describes biointerface and sensing membraneconfigurations and materials that can be applied to the preferredembodiments.

In the illustrated embodiment, the counter electrode is provided tobalance the current generated by the species being measured at theworking electrode. In the case of a glucose oxidase based glucosesensor, the species being measured at the working electrode is H₂O₂.Glucose oxidase catalyzes the conversion of oxygen and glucose tohydrogen peroxide and gluconate according to the following reaction:Glucose+O₂→Gluconate+H₂O₂

The change in H₂O₂ can be monitored to determine glucose concentrationbecause for each glucose molecule metabolized, there is a proportionalchange in the product H₂O₂. Oxidation of H₂O₂ by the working electrodeis balanced by reduction of ambient oxygen, enzyme generated H₂O₂, orother reducible species at the counter electrode. The H₂O₂ produced fromthe glucose oxidase reaction further reacts at the surface of workingelectrode and produces two protons (2H⁺), two electrons (2e⁻), and oneoxygen molecule (O₂).

In one embodiment, a potentiostat is employed to monitor theelectrochemical reaction at the electrochemical cell. The potentiostatapplies a constant potential to the working and reference electrodes todetermine a current value. The current that is produced at the workingelectrode (and flows through the circuitry to the counter electrode) issubstantially proportional to the amount of H₂O₂ that diffuses to theworking electrode. Accordingly, a raw signal can be produced that isrepresentative of the concentration of glucose in the user's body, andtherefore can be utilized to estimate a meaningful glucose value, suchas is described herein.

Sensor Electronics

The following description of sensor electronics associated with theelectronics unit is applicable to a variety of continuous analytesensors, such as non-invasive, minimally invasive, and/or invasive (e.g.transcutaneous and wholly implantable) sensors. For example, the sensorelectronics and data processing as well as the receiver electronics anddata processing described below can be incorporated into the whollyimplantable glucose sensor disclosed in U.S. Patent Publication No.2005-0245799-A1 and U.S. Patent Publication No. US-2006-0015020-A1.

FIG. 14 is a block diagram that illustrates the electronics 132associated with the sensor system 10 in one embodiment. In thisembodiment, a potentiostat 134 is shown, which is operably connected toan electrode system (such as described above) and provides a voltage tothe electrodes, which biases the sensor to enable measurement of ancurrent signal indicative of the analyte concentration in the host (alsoreferred to as the analog portion). In some embodiments, thepotentiostat includes a resistor (not shown) that translates the currentinto voltage. In some alternative embodiments, a current to frequencyconverter is provided that is configured to continuously integrate themeasured current, for example, using a charge counting device.

An A/D converter 136 digitizes the analog signal into a digital signal,also referred to as “counts” for processing. Accordingly, the resultingraw data stream in counts, also referred to as raw sensor data, isdirectly related to the current measured by the potentiostat 134.

A processor module 138 includes the central control unit that controlsthe processing of the sensor electronics 132. In some embodiments, theprocessor module includes a microprocessor, however a computer systemother than a microprocessor can be used to process data as describedherein, for example an ASIC can be used for some or all of the sensor'scentral processing. The processor typically provides semi-permanentstorage of data, for example, storing data such as sensor identifier(ID) and programming to process data streams (for example, programmingfor data smoothing and/or replacement of signal artifacts such as isdescribed in U.S. Patent No. US-2005-0043598-A1. The processoradditionally can be used for the system's cache memory, for example fortemporarily storing recent sensor data. In some embodiments, theprocessor module comprises memory storage components such as ROM, RAM,dynamic-RAM, static-RAM, non-static RAM, EEPROM, rewritable ROMs, flashmemory, or the like.

In some embodiments, the processor module comprises a digital filter,for example, an infinite impulse response (IIR) or finite impulseresponse (FIR) filter, configured to smooth the raw data stream from theA/D converter. Generally, digital filters are programmed to filter datasampled at a predetermined time interval (also referred to as a samplerate). In some embodiments, wherein the potentiostat is configured tomeasure the analyte at discrete time intervals, these time intervalsdetermine the sample rate of the digital filter. In some alternativeembodiments, wherein the potentiostat is configured to continuouslymeasure the analyte, for example, using a current-to-frequency converteras described above, the processor module can be programmed to request adigital value from the A/D converter at a predetermined time interval,also referred to as the acquisition time. In these alternativeembodiments, the values obtained by the processor are advantageouslyaveraged over the acquisition time due the continuity of the currentmeasurement. Accordingly, the acquisition time determines the samplerate of the digital filter. In preferred embodiments, the processormodule is configured with a programmable acquisition time, namely, thepredetermined time interval for requesting the digital value from theA/D converter is programmable by a user within the digital circuitry ofthe processor module. An acquisition time of from about 2 seconds toabout 512 seconds is preferred; however any acquisition time can beprogrammed into the processor module. A programmable acquisition time isadvantageous in optimizing noise filtration, time lag, andprocessing/battery power.

Preferably, the processor module is configured to build the data packetfor transmission to an outside source, for example, an RF transmissionto a receiver as described in more detail below. Generally, the datapacket comprises a plurality of bits that can include a sensor ID code,raw data, filtered data, and/or error detection or correction. Theprocessor module can be configured to transmit any combination of rawand/or filtered data.

In some embodiments, the processor module further comprises atransmitter portion that determines the transmission interval of thesensor data to a receiver, or the like. In some embodiments, thetransmitter portion, which determines the interval of transmission, isconfigured to be programmable. In one such embodiment, a coefficient canbe chosen (e.g. a number of from about 1 to about 100, or more), whereinthe coefficient is multiplied by the acquisition time (or samplingrate), such as described above, to define the transmission interval ofthe data packet. Thus, in some embodiments, the transmission interval isprogrammable between about 2 seconds and about 850 minutes, morepreferably between about 30 second and 5 minutes; however, anytransmission interval can be programmable or programmed into theprocessor module. However, a variety of alternative systems and methodsfor providing a programmable transmission interval can also be employed.By providing a programmable transmission interval, data transmission canbe customized to meet a variety of design criteria (e.g. reduced batteryconsumption, timeliness of reporting sensor values, etc.)

Conventional glucose sensors measure current in the nanoAmp range. Incontrast to conventional glucose sensors, the preferred embodiments areconfigured to measure the current flow in the picoAmp range, and in someembodiments, femtoAmps. Namely, for every unit (mg/dL) of glucosemeasured, at least one picoAmp of current is measured. Preferably, theanalog portion of the A/D converter 136 is configured to continuouslymeasure the current flowing at the working electrode and to convert thecurrent measurement to digital values representative of the current. Inone embodiment, the current flow is measured by a charge counting device(e.g. a capacitor). Thus, a signal is provided, whereby a highsensitivity maximizes the signal received by a minimal amount ofmeasured hydrogen peroxide (e.g. minimal glucose requirements withoutsacrificing accuracy even in low glucose ranges), reducing thesensitivity to oxygen limitations in vivo (e.g. in oxygen-dependentglucose sensors).

A battery 144 is operably connected to the sensor electronics 132 andprovides the power for the sensor. In one embodiment, the battery is alithium manganese dioxide battery; however, any appropriately sized andpowered battery can be used (for example, AAA, nickel-cadmium,zinc-carbon, alkaline, lithium, nickel-metal hydride, lithium-ion,zinc-air, zinc-mercury oxide, silver-zinc, and/or hermetically-sealed).In some embodiments, the battery is rechargeable, and/or a plurality ofbatteries can be used to power the system. The sensor can betranscutaneously powered via an inductive coupling, for example. In someembodiments, a quartz crystal 96 is operably connected to the processor138 and maintains system time for the computer system as a whole, forexample for the programmable acquisition time within the processormodule.

Optional temperature probe 140 is shown, wherein the temperature probeis located on the electronics assembly or the glucose sensor itself. Thetemperature probe can be used to measure ambient temperature in thevicinity of the glucose sensor. This temperature measurement can be usedto add temperature compensation to the calculated glucose value.

An RF module 148 is operably connected to the processor 138 andtransmits the sensor data from the sensor to a receiver within awireless transmission 150 via antenna 152. In some embodiments, a secondquartz crystal 154 provides the time base for the RF carrier frequencyused for data transmissions from the RF transceiver. In some alternativeembodiments, however, other mechanisms, such as optical, infraredradiation (IR), ultrasonic, or the like, can be used to transmit and/orreceive data.

In the RF telemetry module of the preferred embodiments, the hardwareand software are designed for low power requirements to increase thelongevity of the device (for example, to enable a life of from about 3to about 24 months, or more) with maximum RF transmittance from the invivo environment to the ex vivo environment for wholly implantablesensors (for example, a distance of from about one to ten meters ormore). Preferably, a high frequency carrier signal of from about 402 MHzto about 433 MHz is employed in order to maintain lower powerrequirements. Additionally, in wholly implantable devices, the carrierfrequency is adapted for physiological attenuation levels, which isaccomplished by tuning the RF module in a simulated in vivo environmentto ensure RF functionality after implantation; accordingly, thepreferred glucose sensor can sustain sensor function for 3 months, 6months, 12 months, or 24 months or more.

When a sensor is first implanted into host tissue, the sensor andreceiver are initialized. This is referred to as start-up mode, andinvolves optionally resetting the sensor data and calibrating the sensor32. In selected embodiments, mating the electronics unit 16 to themounting unit triggers a start-up mode. In other embodiments, thestart-up mode is triggered by the receiver, which is described in moredetail with reference to FIG. 21, below.

Preferably, the electronics unit 16 indicates to the receiver (FIGS. 15and 17) that calibration is to be initialized (or re-initialized). Theelectronics unit 16 transmits a series of bits within a transmitted datapacket wherein a sensor code can be included in the periodictransmission of the device. The status code is used to communicatesensor status to the receiving device. The status code can be insertedinto any location in the transmitted data packet, with or without othersensor information. In one embodiment, the status code is designed to beunique or near unique to an individual sensor, which can be accomplishedusing a value that increments, decrements, or changes in some way afterthe transmitter detects that a sensor has been removed and/or attachedto the transmitter. In an alternative embodiment, the status code can beconfigured to follow a specific progression, such as a BCDinterpretation of a Gray code.

In some embodiments, the sensor electronics 132 are configured to detecta current drop to zero in the working electrode 44 associated withremoval of a sensor 32 from the host (or the electronics unit 16 fromthe mounting unit 14), which can be configured to trigger an incrementof the status code. If the incremented value reaches a maximum, it canbe designed to roll over to 0. In some embodiments, the sensorelectronics are configured to detect a voltage change cycle associatedwith removal and/or re-insertion of the sensor, which can be sensed inthe counter electrode (e.g. of a three-electrode sensor), which can beconfigured to trigger an increment of the status code.

In some embodiments, the sensor electronics 132 can be configured tosend a special value (for example, 0) that indicates that theelectronics unit is not attached when removal of the sensor (orelectronics unit) is detected. This special value can be used to triggera variety of events, for example, to halt display of analyte values.Incrementing or decrementing routines can be used to skip this specialvalue.

In some embodiments, the electronics unit 16 is configured to includeadditional contacts, which are designed to sense a specific resistance,or passive value, in the sensor system while the electronics unit isattached to the mounting unit. Preferably, these additional contacts areconfigured to detect information about a sensor, for example, whetherthe sensor is operatively connected to the mounting unit, the sensor'sID, a calibration code, or the like. For example, subsequent to sensingthe passive value, the sensor electronics can be configured to changethe sensor ID code by either mapping the value to a specific code, orinternally detecting that the code is different and adjusting the sensorID code in a predictable manner. As another example, the passive valuecan include information on parameters specific to a sensor (such as invitro sensitivity information as described elsewhere herein).

In some embodiments, the electronics unit 16 includes additionalcontacts configured to communicate with a chip disposed in the mountingunit 14. In this embodiment, the chip is designed with a unique ornear-unique signature that can be detected by the electronics unit 16and noted as different, and/or transmitted to the receiver 158 as thesensor ID code.

In some embodiments, the electronics unit 16 is inductively coupled toan RFID or similar chip in the mounting unit 14. In this embodiment, theRFID tag uniquely identifies the sensor 32 and allows the transmitter toadjust the sensor ID code accordingly and/or to transmit the uniqueidentifier to the receiver 158.

In some situations, it can be desirable to wait an amount of time afterinsertion of the sensor to allow the sensor to equilibrate in vivo, alsoreferred to as “break-in.” Accordingly, the sensor electronics can beconfigured to aid in decreasing the break-in time of the sensor byapplying different voltage settings (for example, starting with a highervoltage setting and then reducing the voltage setting) to speed theequilibration process.

In some situations, the sensor may not properly deploy, connect to, orotherwise operate as intended. Accordingly, the sensor electronics canbe configured such that if the current obtained from the workingelectrode, or the subsequent conversion of the current into digitalcounts, for example, is outside of an acceptable threshold, then thesensor is marked with an error flag, or the like. The error flag can betransmitted to the receiver to instruct the user to reinsert a newsensor, or to implement some other error correction.

The above-described detection and transmission methods can beadvantageously employed to minimize or eliminate human interaction withthe sensor, thereby minimizing human error and/or inconvenience.Additionally, the sensors of preferred embodiments do not require thatthe receiver be in proximity to the transmitter during sensor insertion.Any one or more of the above described methods of detecting andtransmitting insertion of a sensor and/or electronics unit can becombined or modified, as is appreciated by one skilled in the art. F

Receiver

FIG. 15 is a perspective view of a sensor system, including wirelesscommunication between a sensor and a receiver. Preferably theelectronics unit 16 is wirelessly connected to a receiver 158 via one-ortwo-way RF transmissions or the like. However, a wired connection isalso contemplated. The receiver 158 provides much of the processing anddisplay of the sensor data, and can be selectively worn and/or removedat the host's convenience. Thus, the sensor system 10 can be discreetlyworn, and the receiver 158, which provides much of the processing anddisplay of the sensor data, can be selectively worn and/or removed atthe host's convenience. Particularly, the receiver 158 includesprogramming for retrospectively and/or prospectively initiating acalibration, converting sensor data, updating the calibration,evaluating received reference and sensor data, and evaluating thecalibration for the analyte sensor, such as described in more detailwith reference to U.S. Patent Publication No. US-2005-0027463-A1.

FIGS. 16A to 16D are schematic views of a receiver in first, second,third, and fourth embodiments, respectively. A receiver 1640 comprisessystems necessary to receive, process, and display sensor data from ananalyte sensor, such as described elsewhere herein. Particularly, thereceiver 1640 can be a pager-sized device, for example, and comprise auser interface that has a plurality of buttons 1642 and a liquid crystaldisplay (LCD) screen 1644, and which can include a backlight. In someembodiments the user interface can also include a keyboard, a speaker,and a vibrator such as described with reference to FIG. 17A.

In some embodiments a user is able to toggle through some or all of thescreens shown in FIGS. 16A to 16D using a toggle button on the receiver.In some embodiments, the user is able to interactively select the typeof output displayed on their user interface. In some embodiments, thesensor output can have alternative configurations.

Receiver Electronics

FIG. 17A is a block diagram that illustrates the configuration of themedical device in one embodiment, including a continuous analyte sensor,a receiver, and an external device. In general, the analyte sensorsystem is any sensor configuration that provides an output signalindicative of a concentration of an analyte (e.g. invasive,minimally-invasive, and/or non-invasive sensors as described above). Theoutput signal is sent to a receiver 158 and received by an input module174, which is described in more detail below. The output signal istypically a raw data stream that is used to provide a useful value ofthe measured analyte concentration to a patient or a doctor, forexample. In some embodiments, the raw data stream can be continuously orperiodically algorithmically smoothed or otherwise modified to diminishoutlying points that do not accurately represent the analyteconcentration, for example due to signal noise or other signalartifacts, such as described in U.S. Pat. No. 6,931,327, which isincorporated herein by reference in its entirety.

Referring again to FIG. 17A, the receiver 158, which is operativelylinked to the sensor system 10, receives a data stream from the sensorsystem 10 via the input module 174. In one embodiment, the input moduleincludes a quartz crystal operably connected to an RF transceiver (notshown) that together function to receive and synchronize data streamsfrom the sensor system 10. However, the input module 174 can beconfigured in any manner that is capable of receiving data from thesensor. Once received, the input module 174 sends the data stream to aprocessor 176 that processes the data stream, such as is described inmore detail below.

The processor 176 is the central control unit that performs theprocessing, such as storing data, analyzing data streams, calibratinganalyte sensor data, estimating analyte values, comparing estimatedanalyte values with time corresponding measured analyte values,analyzing a variation of estimated analyte values, downloading data, andcontrolling the user interface by providing analyte values, prompts,messages, warnings, alarms, or the like. The processor includes hardwareand software that performs the processing described herein, for exampleflash memory provides permanent or semi-permanent storage of data,storing data such as sensor ID, receiver ID, and programming to processdata streams (for example, programming for performing estimation andother algorithms described elsewhere herein) and random access memory(RAM) stores the system's cache memory and is helpful in dataprocessing.

Preferably, the input module 174 or processor module 176 performs aCyclic Redundancy Check (CRC) to verify data integrity, with or withouta method of recovering the data if there is an error. In someembodiments, error correction techniques such as those that use Hammingcodes or Reed-Solomon encoding/decoding methods are employed to correctfor errors in the data stream. In one alternative embodiment, aniterative decoding technique is employed, wherein the decoding isprocessed iteratively (e.g. in a closed loop) to determine the mostlikely decoded signal. This type of decoding can allow for recovery of asignal that is as low as 0.5 dB above the noise floor, which is incontrast to conventional non-iterative decoding techniques (such asReed-Solomon), which requires approximately 3 dB or about twice thesignal power to recover the same signal (e.g. a turbo code).

An output module 178, which is integral with and/or operativelyconnected with the processor 176, includes programming for generatingoutput based on the data stream received from the sensor system 10 andits processing incurred in the processor 176. In some embodiments,output is generated via a user interface 160.

The user interface 160 comprises a keyboard 162, speaker 164, vibrator166, backlight 168, liquid crystal display (LCD) screen 170, and one ormore buttons 172. The components that comprise the user interface 160include controls to allow interaction of the user with the receiver. Thekeyboard 162 can allow, for example, input of user information abouthimself/herself, such as mealtime, exercise, insulin administration,customized therapy recommendations, and reference analyte values. Thespeaker 164 can produce, for example, audible signals or alerts forconditions such as present and/or estimated (e.g., predicted)hyperglycemic or hypoglycemic conditions in a person with diabetes. Thevibrator 166 can provide, for example, tactile signals or alerts forreasons such as described with reference to the speaker, above. Thebacklight 168 can be provided, for example, to aid the user in readingthe LCD 170 in low light conditions. The LCD 170 can be provided, forexample, to provide the user with visual data output, such as isdescribed in U.S. Patent Publication No. US-2005-0203360-A1. FIGS. 17Bto 17D illustrate some additional visual displays that can be providedon the screen 170. In some embodiments, the LCD is a touch-activatedscreen, enabling each selection by a user, for example, from a menu onthe screen. The buttons 172 can provide for toggle, menu selection,option selection, mode selection, and reset, for example. In somealternative embodiments, a microphone can be provided to allow forvoice-activated control.

In some embodiments, prompts or messages can be displayed on the userinterface to convey information to the user, such as reference outliervalues, requests for reference analyte values, therapy recommendations,deviation of the measured analyte values from the estimated analytevalues, or the like. Additionally, prompts can be displayed to guide theuser through calibration or trouble-shooting of the calibration.

Additionally, data output from the output module 178 can provide wiredor wireless, one-or two-way communication between the receiver 158 andan external device 180. The external device 180 can be any device thatwherein interfaces or communicates with the receiver 158. In someembodiments, the external device 180 is a computer, and the receiver 158is able to download historical data for retrospective analysis by thepatient or physician, for example. In some embodiments, the externaldevice 180 is a modem or other telecommunications station, and thereceiver 158 is able to send alerts, warnings, emergency messages, orthe like, via telecommunication lines to another party, such as a doctoror family member. In some embodiments, the external device 180 is aninsulin pen, and the receiver 158 is able to communicate therapyrecommendations, such as insulin amount and time to the insulin pen. Insome embodiments, the external device 180 is an insulin pump, and thereceiver 158 is able to communicate therapy recommendations, such asinsulin amount and time to the insulin pump. The external device 180 caninclude other technology or medical devices, for example pacemakers,implanted analyte sensor patches, other infusion devices, telemetrydevices, or the like. Some additional examples of external devices aredescribed in more detail with reference to FIGS. 53-56.

The user interface 160, including keyboard 162, buttons 172, amicrophone (not shown), and the external device 180, can be configuredto allow input of data. Data input can be helpful in obtaininginformation about the patient (for example, meal time, exercise, or thelike), receiving instructions from a physician (for example, customizedtherapy recommendations, targets, or the like), and downloading softwareupdates, for example. Keyboard, buttons, touch-screen, and microphoneare all examples of mechanisms by which a user can input data directlyinto the receiver. A server, personal computer, personal digitalassistant, insulin pump, and insulin pen are examples of externaldevices that can provide useful information to the receiver. Otherdevices internal or external to the sensor that measure other aspects ofa patient's body (for example, temperature sensor, accelerometer, heartrate monitor, oxygen monitor, or the like) can be used to provide inputhelpful in data processing. In one embodiment, the user interface canprompt the patient to select an activity most closely related to theirpresent activity, which can be helpful in linking to an individual'sphysiological patterns, or other data processing. In another embodiment,a temperature sensor and/or heart rate monitor can provide informationhelpful in linking activity, metabolism, and glucose excursions of anindividual. While a few examples of data input have been provided here,a variety of information can be input, which can be helpful in dataprocessing.

FIG. 17B is an illustration of an LCD screen 170 showing continuous andsingle point glucose information in the form of a trend graph 184 and asingle numerical value 186. The trend graph shows upper and lowerboundaries 182 representing a target range between which the host shouldmaintain his/her glucose values. Preferably, the receiver is configuredsuch that these boundaries 182 can be configured or customized by auser, such as the host or a care provider. By providing visualboundaries 182, in combination with continuous analyte values over time(e.g., a trend graph 184), a user can better learn how to controlhis/her analyte concentration (e.g., a person with diabetes can betterlearn how to control his/her glucose concentration) as compared tosingle point (single numerical value 186) alone. Although FIG. 17Billustrates a 1-hour trend graph (e.g., depicted with a time range 188of 1-hour), a variety of time ranges can be represented on the screen170, for example, 3-hour, 9-hour, 1-day, and the like.

FIG. 17C is an illustration of an LCD screen 170 showing a low alertscreen that can be displayed responsive to a host's analyteconcentration falling below a lower boundary (see boundaries 182). Inthis exemplary screen, a host's glucose concentration has fallen to 55mg/dL, which is below the lower boundary set in FIG. 17B, for example.The arrow 190 represents the direction of the analyte trend, forexample, indicating that the glucose concentration is continuing todrop. The annotation 192 (“LOW”) is helpful in immediately and clearlyalerting the host that his/her glucose concentration has dropped below apreset limit, and what may be considered to be a clinically safe value,for example. FIG. 17D is an illustration of an LCD screen 170 showing ahigh alert screen that can be displayed responsive to a host's analyteconcentration rising above an upper boundary (see boundaries 182). Inthis exemplary screen, a host's glucose concentration has risen to 200mg/dL, which is above a boundary set by the host, thereby triggering thehigh alert screen. The arrow 190 represents the direction of the analytetrend, for example, indicating that the glucose concentration iscontinuing to rise. The annotation 192 (“HIGH”) is helpful inimmediately and clearly alerting the host that his/her glucoseconcentration has above a preset limit, and what may be considered to bea clinically safe value, for example.

Although a few exemplary screens are depicted herein, a variety ofscreens can be provided for illustrating any of the informationdescribed in the preferred embodiments, as well as additionalinformation. A user can toggle between these screens (e.g., usingbuttons 172) and/or the screens can be automatically displayedresponsive to programming within the receiver 158, and can besimultaneously accompanied by another type of alert (audible or tactile,for example).

In some embodiments the receiver 158 can have a length of from about 8cm to about 15 cm, a width of from about 3.5 cm to about 10 cm, and/or athickness of from about 1 cm to about 3.5 cm. In some embodiments thereceiver 158 can have a volume of from about 120 cm³ to about 180 cm³,and can have a weight of from about 70 g to 130 g. The dimensions andvolume can be higher or lower, depending, e.g., on the type of devicesintegrated (e.g., finger stick devices, pumps, PDAs, and the like.), thetype of user interface employed, and the like.

In some embodiments, the receiver 158 is an application-specific device.In some embodiments the receiver 158 can be a device used for otherfunctions, such as are described in U.S. Pat. No. 6,558,320. Forexample, the receiver 158 can be integrated into a personal computer(PC), a personal digital assistant (PDA), a cell phone, or another fixedor portable computing device. The integration of the receiver 158function into a more general purpose device can comprise the addition ofsoftware and/or hardware to the device. Communication between the sensorelectronics 16 and the receiver 158 function of the more general purposedevice can be implemented with wired or wireless technologies. Forexample, a PDA can be configured with a data communications port and/ora wireless receiver. After the user establishes a communication linkbetween the electronics unit 16 and the PDA, the electronics unit 16transmits data to the PDA which then processes the data according tosoftware which has been loaded thereon so as to display.

Algorithms

FIG. 18A provides a flow chart 200 that illustrates the initialcalibration and data output of the sensor data in one embodiment,wherein calibration is responsive to reference analyte data. Initialcalibration, also referred to as start-up mode, occurs at theinitialization of a sensor, for example, the first time an electronicsunit is used with a particular sensor. In certain embodiments, start-upcalibration is triggered when the system determines that it can nolonger remain in normal or suspended mode, which is described in moredetail with reference to FIG. 21.

Calibration of an analyte sensor comprises data processing that convertssensor data signal into an estimated analyte measurement that ismeaningful to a user. Accordingly, a reference analyte value is used tocalibrate the data signal from the analyte sensor.

At block 202, a sensor data receiving module, also referred to as thesensor data module, receives sensor data (e.g. a data stream), includingone or more time-spaced sensor data points, from the sensor 32 via thereceiver 158, which can be in wired or wireless communication with thesensor 32. The sensor data point(s) can be smoothed (filtered) incertain embodiments using a filter, for example, a finite impulseresponse (FIR) or infinite impulse response (IIR) filter. During theinitialization of the sensor, prior to initial calibration, the receiverreceives and stores the sensor data, however it can be configured to notdisplay any data to the user until initial calibration and, optionally,stabilization of the sensor has been established. In some embodiments,the data stream can be evaluated to determine sensor break-in(equilibration of the sensor in vitro or in vivo).

At block 204, a reference data receiving module, also referred to as thereference input module, receives reference data from a reference analytemonitor, including one or more reference data points. In one embodiment,the reference analyte points can comprise results from a self-monitoredblood analyte test (e.g., finger stick test). For example, the user canadminister a self-monitored blood analyte test to obtain an analytevalue (e.g., point) using any known analyte sensor, and then enter thenumeric analyte value into the computer system. Alternatively, aself-monitored blood analyte test is transferred into the computersystem through a wired or wireless connection to the receiver (e.g.computer system) so that the user simply initiates a connection betweenthe two devices, and the reference analyte data is passed or downloadedbetween the self-monitored blood analyte test and the receiver.

In yet another embodiment, the self-monitored analyte monitor (e.g.,SMBG) is integral with the receiver so that the user simply provides ablood sample to the receiver, and the receiver runs the analyte test todetermine a reference analyte value, such as is described in more detailherein and with reference to U.S. Patent Publication No.US-2005-0154271-A1, which is incorporated herein by reference in itsentirety and which describes some systems and methods for integrating areference analyte monitor into a receiver for a continuous analytesensor.

In some embodiments, the integrated receiver comprises a microprocessorwhich can be programmed to process sensor data to perform thecalibration. Such programming, which can be stored in a computerreadable memory, can also comprise data acceptability testing usingcriteria such as that discussed above with reference to FIG. 18A. Forexample the microprocessor can be programmed so as to determine the rateof change of glucose concentration based on the continuous sensor data,and perform calibration only if the rate of change is below apredetermined threshold, such as 2 mg/dL/min. In some embodiments thereceiver can also comprise modules to perform a calibration proceduresuch as is described herein. Such modules include, but are not limitedto an input module, a data matching module, a calibration module, aconversion function module, a sensor data transformation module, acalibration evaluation module, a clinical module, a stability module,and a user interface, each of which have been described herein.

The monitor can be of any suitable configuration. For example, in oneembodiment, the reference analyte points can comprise results from aself-monitored blood analyte test (e.g., from a finger stick test), suchas those described in U.S. Pat. Nos. 6,045,567; 6,156,051; 6,197,040;6,284,125; 6,413,410; and 6,733,655. In one such embodiment, the usercan administer a self-monitored blood analyte test to obtain an analytevalue (e.g., point) using any suitable analyte sensor, and then enterthe numeric analyte value into the computer system (e.g., the receiver).In another such embodiment, a self-monitored blood analyte testcomprises a wired or wireless connection to the receiver (e.g. computersystem) so that the user simply initiates a connection between the twodevices, and the reference analyte data is passed or downloaded betweenthe self-monitored blood analyte test and the receiver. In yet anothersuch embodiment, the self-monitored analyte test is integral with thereceiver so that the user simply provides a blood sample to thereceiver, and the receiver runs the analyte test to determine areference analyte value.

Other suitable monitor configurations include, for example, thosedescribed in U.S. Pat. Nos. 4,994,167, 4,757,022, 6,551,494. Inalternative embodiments, the single point glucose monitor of thisparticular embodiment can be configured as described with reference toU.S. Patent Publication No. US-2005-0154271-A1. In other alternativeembodiments, the monitor can be configured using other glucose meterconfigurations. Numerous advantages associated with the integratedreceiver, such as ensuring accurate time stamping of the single pointglucose test at the receiver and other advantages described herein, canbe provided by an integrated continuous glucose receiver and singlepoint glucose monitor, such as described herein.

FIGS. 18B to 18F illustrate another embodiment of an integratedreceiver, wherein a single point glucose monitor includes a stylusmovably mounted to the integrated receiver for measurement of glucose ina biological sample. FIG. 18B is a perspective view of the integratedreceiver housing in another embodiment, showing a single point glucosemonitor including a stylus movably mounted to the integrated receiver,wherein the stylus is shown in a storage position. FIG. 18C is aperspective view of the integrated housing of FIG. 18B, showing thestylus in a testing position. FIG. 18D is a perspective view of aportion of the stylus of FIG. 18B, showing the sensing region. FIG. 18Eis a perspective view of the integrated receiver housing of FIG. 18B,showing the stylus loaded with a disposable film, and in its testingposition. FIG. 18F is a perspective view of a portion of the stylus ofFIG. 18B, showing the sensing region with a disposable film stretchedand/or disposed thereon.

In this embodiment, the integrated receiver provides 1892 a housing thatintegrates a single point glucose monitor 1894 and electronics (see FIG.8) useful to receive, process, and display data on the user interface1896. The single point glucose monitor 1894 permits rapid and accuratemeasurement of the amount of a particular substance (for example,glucose) in a biological fluid. Generally, the integrated receiverelectronics process single point glucose monitor data, receive andprocess continuous glucose sensor data, including calibration of thecontinuous sensor data using the single point monitor data for example,and output data via the user interface 1896, such as is described belowin more detail with reference to FIG. 8.

The single point glucose monitor 1894 includes a stylus 1898 that ismovably mounted to the integrated receiver housing 1892 via a connector1893. The connector 1893 can be a cord, bar, hinge, or any suchconnection means that allows the stylus to move from a first (storage)position (FIG. 18B) to a second (testing) position (FIG. 18C) on thehousing. The stylus is not constrained to the first and secondpositions; rather the stylus can be configured to swing at variousangles, about various pivots, or in any manner allowed by the connectorfor convenience to the user. In some alternative embodiments, the stylus1898 is removably mounted on the integrated receiver housing 1892 and anoperable connection can be established using a wireless connection, oralternatively using electrical contacts that operably connect the stylus1898 that is removably mounted onto the integrated receiver housing1892.

The stylus 1898 includes a sensing region 18100 on one end that isoperably connected to the integrated receiver's electronics (FIG. 8). Asillustrated in FIG. 18D, the sensing region 18100 is provided with atleast two, preferably three electrodes 18102. In some embodiments asensing membrane (not shown) is also disposed over the electrodes 18102and/or the entire sensing region 18100. The sensing region includes theelectrodes 18102 and the sensing membrane, which are configured tomeasure glucose in a manner such as described above with reference tothe sensing region of FIGS. 2 and 4. In one embodiment, the sensingmembrane is reusable and can be held on the sensing region 18100 by aclip, such as described with reference to FIG. 2. In alternativeembodiments, the sensing membrane is reusable and can be disposed ontothe sensing region using depositing or bonding techniques known in theart of polymers. In some embodiments the sensing membrane can bedisposable or suitable for a single use.

In some embodiments, so as to maintain a preferred moisture condition ofthe sensing region 18100, and particularly of the sensing membrane, theintegrated receiver housing 1892 includes a moisturizing solutionchamber (not shown) located at the end of the receiving chamber 18104that receives the stylus for storage, such that when the stylus is inits storage position (FIG. 18B), the sensing membrane is maintained inthe moisturizing solution. A moisturizing solution port 18106 is incommunication with the moisturizing solution chamber and allows forrefilling of the moisturizing solution chamber using a moisturizingrefill solution 18108. In some embodiments the moisturizing solution canbe a sterile solution.

In some embodiments, additional to or alternative to maintaining thesensing region 18100 in a moisturizing solution chamber, a moisturizingsolution can be applied to the sensing region 18100 at or around thetime of sensing. For example, a user can apply a moisturizing solutionto the sensing region 18100 just before applying the sensing region18100 to an area of skin. Also, the user can apply the moisturizingsolution to the area of skin.

When a biological sample 18106 (FIG. 18F) is placed on a surface, suchas the surface of the sensing membrane and/or sensing region 18100,there contamination of the surface after use of the biological sample18106 can be a concern. Accordingly, in some embodiments, a single-usedisposable bioprotective film 18109 can be placed over the sensingregion 18100 to provide protection from contamination. The bioprotectivefilm 18109 can be any film with that allows the passage of glucose, butblocks the passage of undesired species in the blood that could damageor contaminate the sensing membrane and/or cause inaccurate measurements(for example, a thin film of very low molecular weight cutoff to preventthe transport of proteins, viruses, and the like). In some embodimentsthe bioprotective film 18109 is not single-use disposable, but can betreated and reused.

In some alternative embodiments, the bioprotective film 18109 furthercomprises a sensing membrane formed as a part of the film (for example,laminated to the film), instead of (or in addition to) a sensingmembrane disposed on the sensing region. This alternative embodiment isparticularly advantageous in that it provides a disposable sensingmembrane that requires no cleaning step, for example.

Because the stylus 1898 can be put into direct contact with thebiological sample 18106 (for example, on a finger or arm), no transfermechanism is required, and therefore the sample size can be smaller thanconventionally required. Additionally, sensing region 18100 may notrequire a separate cleaning step, because the bioprotective film 18109fully protects the sensing region 18100 from contamination.

The integrated receiver 1892 housing further allows for storage anddispensing of the disposable films 18109. A shuttle mechanism 18110 isprovided that preferably feeds the films 18109 into a spring-loadedstorage chamber (not shown) beneath the shuttle mechanism 18110, or thelike. The shuttle mechanism 18110 can be used to load the disposablefilms 18109, one at a time, into a dispensing chamber 18111 fordispensing onto the sensing region. In alternative embodiments, otherstorage and dispensing mechanisms can be configured as a part of theintegrated receiver housing 1812 or separate therefrom.

In practice, the stylus 1898 is held in its storage position within thereceiving chamber 18104 where it is protected and maintained with apreferred moisture condition (FIG. 18B). A user then withdrawals thestylus 1898 from the receiving chamber 18104 (FIG. 18C) and loads adisposable film 18109 by sliding the shuttle mechanism 18110 toward thedispensing chamber 18111. When the sensing region 18100 of the stylus1898 presses on the disposable film 18109 within the dispensing chamber,the film will be stretched over and/or otherwise stick to the moistsensing membrane on the surface of the sensing region 18100 (FIG. 18E).At this point, the stylus 1898 is ready for a biological sample (forexample, a blood sample) 18106. The stylus 1898 can be brought intocontact with the finger or arm of the user to directly receive thebiological sample from the user without the need for a transfermechanism (FIG. 18F). After the test, the bioprotective film 18109 isremoved from the sensing region and the stylus 1898 is replaced into thereceiving chamber 18104 of the integrated receiver 1892.

In some alternative embodiments, the reference data is based on sensordata from another substantially continuous analyte sensor, e.g. atranscutaneous analyte sensor described herein, or another type ofsuitable continuous analyte sensor. In an embodiment employing a seriesof two or more transcutaneous (or other continuous) sensors, the sensorscan be employed so that they provide sensor data in discrete oroverlapping periods. In such embodiments, the sensor data from onecontinuous sensor can be used to calibrate another continuous sensor, orbe used to confirm the validity of a subsequently employed continuoussensor.

In some embodiments, reference data can be subjected to “outlierdetection” wherein the accuracy of a received reference analyte data isevaluated as compared to time-corresponding sensor data. In oneembodiment, the reference data is compared to the sensor data on amodified Clarke Error Grid (e.g. a test similar to the Clarke Error Gridexcept the boundaries between the different regions are modifiedslightly) to determine if the data falls within a predeterminedthreshold. If the data is not within the predetermined threshold, thenthe receiver can be configured to request additional reference analytedata. If the additional reference analyte data confirms (e.g. closelycorrelates to) the first reference analyte data, then the first andsecond reference values are assumed to be accurate and calibration ofthe sensor is adjusted or re-initialized. Alternatively, if the secondreference analyte value falls within the predetermined threshold, thenthe first reference analyte value is assumed to be an outlier and thesecond reference analyte value is used by the algorithm(s) instead. Inone alternative embodiments of outlier detection, projection is used toestimate an expected analyte value, which is compared with the actualvalue and a delta evaluated for substantial correspondence. However,other methods of outlier detection are possible.

Certain acceptability parameters can be set for reference valuesreceived from the user. In some embodiments, the calibration processmonitors the continuous analyte sensor data stream to determine apreferred time for capturing reference analyte concentration values forcalibration of the continuous sensor data stream. In an example whereinthe analyte sensor is a continuous glucose sensor, when data (forexample, observed from the data stream) changes too rapidly, thereference glucose value may not be sufficiently reliable for calibrationdue to unstable glucose changes in the host. In contrast, when sensorglucose data are relatively stable (for example, relatively low rate ofchange), a reference glucose value can be taken for a reliablecalibration. For example, in one embodiment, the receiver can beconfigured to only accept reference analyte values of from about 40mg/dL to about 400 mg/dL. As another example, the receiver can beconfigured to only accept reference analyte values when the rate ofchange is less than a predetermined maximum, such as 1, 1.5, 2, 2.5, 3,or 3.5, mg/dL/min. As yet another example, the receiver can beconfigured to only accept reference analyte values when the rate ofacceleration (or deceleration) is less than a predetermined maximum,such as 0.01 mg/dL/min², 0.02 mg/dL/min², 0.03 mg/dL/min², 0.04mg/dL/min², or 0.05 mg/dL/min² or more.

In some embodiments, the reference data is pre-screened according toenvironmental and/or physiological issues, such as time of day, oxygenconcentration, postural effects, and patient-entered environmental data.In one example embodiment, wherein the sensor comprises an implantableglucose sensor, an oxygen sensor within the glucose sensor is used todetermine if sufficient oxygen is being provided to successfullycomplete the necessary enzyme and electrochemical reactions for glucosesensing. In another example wherein the sensor comprises an implantableglucose sensor, the counter electrode could be monitored for a“rail-effect,” that is, when insufficient oxygen is provided at thecounter electrode causing the counter electrode to reach operational(e.g., circuitry) limits. In some embodiments the receiver is configuredsuch that when conditions for accepting reference analyte values are notmet, the user is notified. Such notice can include an indication as tothe cause of the unacceptability, such as low oxygen or high rate ofanalyte value change. In some embodiments the indication can alsoinclude an indication of suggested corrective action, such as moderatelyincreasing muscular activity so as to increase oxygen levels or to waituntil the rate of analyte value change reduces to an acceptable value.

In one embodiment, the calibration process can prompt the user via theuser interface to “calibrate now” when the reference analyte values areconsidered acceptable. In some embodiments, the calibration process canprompt the user via the user interface to obtain a reference analytevalue for calibration at intervals, for example when analyteconcentrations are at high and/or low values. In some additionalembodiments, the user interface can prompt the user to obtain areference analyte value for calibration based at least in part uponcertain events, such as meals, exercise, large excursions in analytelevels, faulty or interrupted data readings, or the like. In someembodiments, the algorithms can provide information useful indetermining when to request a reference analyte value. For example, whenanalyte values indicate approaching clinical risk, the user interfacecan prompt the user to obtain a reference analyte value.

In yet another example embodiment, the patient is prompted to enter datainto the user interface, such as meal times and/or amount of exercise,which can be used to determine likelihood of acceptable reference data.Evaluation data, such as described in the paragraphs above, can be usedto evaluate an optimum time for reference analyte measurement.Correspondingly, the user interface can then prompt the user to providea reference data point for calibration within a given time period.Consequently, because the receiver proactively prompts the user duringoptimum calibration times, the likelihood of error due to environmentaland physiological limitations may decrease and consistency andacceptability of the calibration may increase.

In some embodiments, the calibration process monitors the continuousanalyte sensor data stream to determine a preferred time for capturingreference analyte concentration values for calibration of the continuoussensor data stream. In an example wherein the analyte sensor is acontinuous glucose sensor, when data (for example, observed from thedata stream) changes too rapidly, the reference glucose value may not besufficiently reliable for calibration due to unstable glucose changes inthe host. In contrast, when sensor glucose data are relatively stable(for example, relatively low rate of change), a reference glucose valuecan be taken for a reliable calibration. In one embodiment, thecalibration process can prompt the user via the user interface to“calibrate now” when the analyte sensor is considered stable.

At block 206, a data matching module, also referred to as the processormodule, matches reference data (e.g. one or more reference analyte datapoints) with substantially time corresponding sensor data (e.g. one ormore sensor data points) to provide one or more matched data pairs. Onereference data point can be matched to one time corresponding sensordata point to form a matched data pair. Alternatively, a plurality ofreference data points can be averaged (e.g. equally or non-equallyweighted average, mean-value, median, or the like) and matched to onetime corresponding sensor data point to form a matched data pair, onereference data point can be matched to a plurality of time correspondingsensor data points averaged to form a matched data pair, or a pluralityof reference data points can be averaged and matched to a plurality oftime corresponding sensor data points averaged to form a matched datapair.

In one embodiment, time corresponding sensor data comprises one or moresensor data points that occur from about 0 minutes to about 20 minutesafter the reference analyte data time stamp (e.g. the time that thereference analyte data is obtained). In one embodiment, a 5-minute timedelay is chosen to compensate for a system time-lag (e.g. the timenecessary for the analyte to diffusion through a membrane(s) of ananalyte sensor). In alternative embodiments, the time correspondingsensor value can be greater than or less than that of theabove-described embodiment, for example ±60 minutes. Variability in timecorrespondence of sensor and reference data can be attributed to, forexample, a longer or shorter time delay introduced by the data smoothingfilter, or if the configuration of the analyte sensor incurs a greateror lesser physiological time lag.

In some implementations of the sensor, the reference analyte data isobtained at a time that is different from the time that the data isinput into the receiver. Accordingly, the “time stamp” of the referenceanalyte (e.g., the time at which the reference analyte value wasobtained) is not the same as the time at which the receiver obtained thereference analyte data. Therefore, some embodiments include a time stamprequirement that ensures that the receiver stores the accurate timestamp for each reference analyte value, that is, the time at which thereference value was actually obtained from the user.

In certain embodiments, tests are used to evaluate the best-matched pairusing a reference data point against individual sensor values over apredetermined time period (e.g. about 30 minutes). In one suchembodiment, the reference data point is matched with sensor data pointsat 5-minute intervals and each matched pair is evaluated. The matchedpair with the best correlation can be selected as the matched pair fordata processing. In some alternative embodiments, matching a referencedata point with an average of a plurality of sensor data points over apredetermined time period can be used to form a matched pair.

In certain embodiments, the data matching module only forms matchedpairs when a certain analyte value condition is satisfied. Such acondition can include any of the conditions discussed above withreference to embodiments pre-screening or conditionally acceptingreference analyte value data at block 204.

At block 208, a calibration set module, also referred to as thecalibration module or processor module, forms an initial calibration setfrom a set of one or more matched data pairs, which are used todetermine the relationship between the reference analyte data and thesensor analyte data. The matched data pairs, which make up the initialcalibration set, can be selected according to predetermined criteria.The criteria for the initial calibration set can be the same as, ordifferent from, the criteria for the updated calibration sets. Incertain embodiments, the number (n) of data pair(s) selected for theinitial calibration set is one. In other embodiments, n data pairs areselected for the initial calibration set wherein n is a function of thefrequency of the received reference data points. In various embodiments,two data pairs make up the initial calibration set or six data pairsmake up the initial calibration set. In an embodiment wherein asubstantially continuous analyte sensor provides reference data,numerous data points are used to provide reference data from more than 6data pairs (e.g. dozens or even hundreds of data pairs). In oneexemplary embodiment, a substantially continuous analyte sensor provides288 reference data points per day (every five minutes for twenty-fourhours), thereby providing an opportunity for a matched data pair 288times per day, for example. While specific numbers of matched data pairsare referred to in the preferred embodiments, any suitable number ofmatched data pairs per a given time period can be employed.

In certain embodiments, the data pairs are selected only when a certainanalyte value condition is satisfied. Such a condition can include anyof the conditions discussed above with reference to embodimentspre-screening or conditionally accepting reference analyte value data atblock 204. In certain embodiments, the data pairs that form the initialcalibration set are selected according to their time stamp, for example,by waiting a predetermined “break-in” time period after implantation,the stability of the sensor data can be increased. In certainembodiments, the data pairs that form the initial calibration set arespread out over a predetermined time period, for example, a period oftwo hours or more. In certain embodiments, the data pairs that form theinitial calibration set are spread out over a predetermined glucoserange, for example, spread out over a range of at least 90 mg/dL ormore.

At block 210, a conversion function module, also referred to as theconversion module or processor module, uses the calibration set tocreate a conversion function. The conversion function substantiallydefines the relationship between the reference analyte data and theanalyte sensor data.

A variety of known methods can be used with the preferred embodiments tocreate the conversion function from the calibration set. In oneembodiment, wherein a plurality of matched data points form thecalibration set, a linear least squares regression is used to calculatethe conversion function; for example, this regression calculates a slopeand an offset using the equation y=mx+b. A variety of regression orother conversion schemes can be implemented herein.

In certain embodiments, the conversion function module only creates aconversion function when a certain analyte value condition is satisfied.Such a condition can include any of the conditions discussed above withreference to embodiments pre-screening or conditionally acceptingreference analyte value data at block 204 or with reference to selectingdata pairs at block 208.

In some alternative embodiments, the sensor is calibrated with asingle-point through the use of a dual-electrode system to simplifysensor calibration. In one such dual-electrode system, a first electrodefunctions as a hydrogen peroxide sensor including a membrane systemcontaining glucose-oxidase disposed thereon, which operates as describedherein. A second electrode is a hydrogen peroxide sensor that isconfigured similar to the first electrode, but with a modified membranesystem (with the enzyme domain removed, for example). This secondelectrode provides a signal composed mostly of the baseline signal, b.

In some dual-electrode systems, the baseline signal is (electronicallyor digitally) subtracted from the glucose signal to obtain a glucosesignal substantially without baseline. Accordingly, calibration of theresultant difference signal can be performed by solving the equationy=mx with a single paired measurement. Calibration of the implantedsensor in this alternative embodiment can be made less dependent on thevalues/range of the paired measurements, less sensitive to error inmanual blood glucose measurements, and can facilitate the sensor's useas a primary source of glucose information for the user. U.S. PatentPublication No. US-2005-0143635-A1 describes systems and methods forsubtracting the baseline from a sensor signal.

In some alternative dual-electrode system embodiments, the analytesensor is configured to transmit signals obtained from each electrodeseparately (e.g., without subtraction of the baseline signal). In thisway, the receiver can process these signals to determine additionalinformation about the sensor and/or analyte concentration. For example,by comparing the signals from the first and second electrodes, changesin baseline and/or sensitivity can be detected and/or measured and usedto update calibration (e.g., without the use of a reference analytevalue). In one such example, by monitoring the corresponding first andsecond signals over time, an amount of signal contributed by baselinecan be measured. In another such example, by comparing fluctuations inthe correlating signals over time, changes in sensitivity can bedetected and/or measured.

In some alternative embodiments, a regression equation y=mx+b is used tocalculate the conversion function; however, prior information can beprovided for m and/or b, thereby enabling calibration to occur withfewer paired measurements. In one calibration technique, priorinformation (e.g., obtained from in vivo or in vitro tests) determines asensitivity of the sensor and/or the baseline signal of the sensor byanalyzing sensor data from measurements taken by the sensor (e.g., priorto inserting the sensor). For example, if there exists a predictiverelationship between in vitro sensor parameters and in vivo parameters,then this information can be used by the calibration procedure. Forexample, if a predictive relationship exists between in vitrosensitivity and in vivo sensitivity, m≈f(m_(in vitro)) then thepredicted m can be used, along with a single matched pair, to solve forb (b=y−mx). If, in addition, b can be assumed=0, for example with adual-electrode configuration that enables subtraction of the baselinefrom the signal such as described above, then both m and b are known apriori, matched pairs are not needed for calibration, and the sensor canbe completely calibrated e.g. without the need for reference analytevalues (e.g. values obtained after implantation in vivo.)

In another alternative embodiment, prior information can be provided toguide or validate the baseline (b) and/or sensitivity (m) determinedfrom the regression analysis. In this embodiment, boundaries can be setfor the regression line that defines the conversion function such thatworking sensors are calibrated accurately and easily (with two points),and non-working sensors are prevented from being calibrated. If theboundaries are drawn too tightly, a working sensor may not enter intocalibration. Likewise, if the boundaries are drawn too loosely, thescheme can result in inaccurate calibration or can permit non-workingsensors to enter into calibration. For example, subsequent to performingregression, the resulting slope and/or baseline are tested to determinewhether they fall within a predetermined acceptable threshold(boundaries). These predetermined acceptable boundaries can be obtainedfrom in vivo or in vitro tests (e.g. by a retrospective analysis ofsensor sensitivities and/or baselines collected from a set ofsensors/patients, assuming that the set is representative of futuredata).

If the slope and/or baseline fall within the predetermined acceptableboundaries, then the regression is considered acceptable and processingcontinues to the next step (e.g. block 212). Alternatively, if the slopeand/or baseline fall outside the predetermined acceptable boundaries,steps can be taken to either correct the regression or fail-safe suchthat a system will not process or display errant data. This can beuseful in situations wherein regression results in errant slope orbaseline values. For example, when points (matched pairs) used forregression are too close in value, the resulting regressionstatistically is less accurate than when the values are spread fartherapart. As another example, a sensor that is not properly deployed or isdamaged during deployment can yield a skewed or errant baseline signal.

In some alternative embodiments, the sensor system does not requireinitial and/or update calibration by the host; in these alternativeembodiments, also referred to as “zero-point calibration” embodiments,use of the sensor system without requiring a reference analytemeasurement for initial and/or update calibration is enabled. Ingeneral, the systems and methods of the preferred embodiments providefor stable and repeatable sensor manufacture, particularly when tightlycontrolled manufacturing processes are utilized. Namely, a batch ofsensors of the preferred embodiments can be designed with substantiallythe same baseline (b) and/or sensitivity (m) (+/−10%) when tested invitro. Additionally, the sensor of the preferred embodiments can bedesigned for repeatable m and b in vivo. Thus, an initial calibrationfactor (conversion function) can be programmed into the sensor (sensorelectronics and/or receiver electronics) that enables conversion of rawsensor data into calibrated sensor data solely using informationobtained prior to implantation (namely, initial calibration does notrequire a reference analyte value). Additionally, to obviate the needfor recalibration (update calibration) during the life of the sensor,the sensor is designed to minimize drift of the sensitivity and/orbaseline over time in vivo. Accordingly, the preferred embodiments canbe manufactured for zero point calibration.

FIG. 18B is a graph that illustrates one example of using priorinformation for slope and baseline. The x-axis represents referenceglucose data (blood glucose) from a reference glucose source in mg/dL;the y-axis represents sensor data from a transcutaneous glucose sensorof the preferred embodiments in counts. An upper boundary line 215 is aregression line that represents an upper boundary of “acceptability” inthis example; the lower boundary line 216 is a regression line thatrepresents a lower boundary of “acceptability” in this example. Theboundary lines 215, 216 were obtained from retrospective analysis of invivo sensitivities and baselines of glucose sensors as described in thepreferred embodiments.

A plurality of matched data pairs 217 represents data pairs in acalibration set obtained from a glucose sensor as described in thepreferred embodiments. The matched data pairs are plotted according totheir sensor data and time-corresponding reference glucose data. Aregression line 218 represents the result of regressing the matched datapairs 217 using least squares regression. In this example, theregression line falls within the upper and lower boundaries 215, 216indicating that the sensor calibration is acceptable.

However, if the slope and/or baseline had fallen outside thepredetermined acceptable boundaries, which would be illustrated in thisgraph by a line that crosses the upper and/or lower boundaries 215, 216,then the system is configured to assume a baseline value and re-run theregression (or a modified version of the regression) with the assumedbaseline, wherein the assumed baseline value is derived from in vivo orin vitro testing. Subsequently, the newly derived slope and baseline areagain tested to determine whether they fall within the predeterminedacceptable boundaries. Similarly, the processing continues in responseto the results of the boundary test. In general, for a set of matchedpairs (e.g., calibration set), regression lines with higher slope(sensitivity) have a lower baseline and regression lines with lowerslope (sensitivity) have a higher baseline. Accordingly, the step ofassuming a baseline and testing against boundaries can be repeated usinga variety of different assumed baselines based on the baseline,sensitivity, in vitro testing, and/or in vivo testing. For example, if aboundary test fails due to high sensitivity, then a higher baseline isassumed and the regression re-run and boundary-tested. It is preferredthat after about two iterations of assuming a baseline and/orsensitivity and running a modified regression, the system assumes anerror has occurred (if the resulting regression lines fall outside theboundaries) and fail-safe. The term “fail-safe” includes modifying thesystem processing and/or display of data responsive to a detected erroravoid reporting of inaccurate or clinically irrelevant analyte values.

In these various embodiments utilizing an additional electrode, priorinformation (e.g. in vitro or in vivo testing), signal processing, orother information for assisting in the calibration process can be usedalone or in combination to reduce or eliminate the dependency of thecalibration on reference analyte values obtained by the host.

At block 212, a sensor data transformation module, also referred to asthe calibration module, conversion module, or processor module, uses theconversion function to transform sensor data into substantiallyreal-time analyte value estimates, also referred to as calibrated data,or converted sensor data, as sensor data is continuously (orintermittently) received from the sensor. For example, the sensor data,which can be provided to the receiver in “counts,” is translated in toestimate analyte value(s) in mg/dL. In other words, the offset value atany given point in time can be subtracted from the raw value (e.g. incounts) and divided by the slope to obtain the estimate analyte value:

${{mg}\text{/}{dL}} = \frac{\left( {{{raw}\mspace{14mu}{value}} - {offset}} \right)}{slope}$

In one embodiment, the conversion function can be used to estimateanalyte values for a future time period by forward projection. Inalternative preferred embodiments, such as are described with referenceto FIGS. 24 to 40, the processor can provide intelligent estimation,including dynamic determination of an algorithm, physiologicalboundaries, evaluation of the estimative algorithm, analysis ofvariations associated with the estimation, and comparison of measuredanalyte values with time corresponding estimated analyte values.

In some alternative embodiments, the sensor and/or reference analytevalues are stored in a database for retrospective analysis.

In certain embodiments, the sensor data transformation module onlyconverts sensor data points into calibrated data points when a certainanalyte value condition is satisfied. Such a condition can include anyof the conditions discussed above with reference to embodimentspre-screening or conditionally accepting reference analyte value data atblock 204, with reference to selecting data pairs at block 208, or withreference to creating a conversion function at block 210.

At block 214, an output module provides output to the user via the userinterface. The output is representative of the estimated analyte value,which is determined by converting the sensor data into a meaningfulanalyte value. User output can be in the form of a numeric estimatedanalyte value, an indication of directional trend of analyteconcentration, and/or a graphical representation of the estimatedanalyte data over a period of time, for example. Other representationsof the estimated analyte values are also possible, for example audio andtactile.

In one exemplary embodiment, such as is shown in FIG. 16A, the estimatedanalyte value is represented by a numeric value. In other exemplaryembodiments, such as are shown in FIGS. 16B to 16D, the user interfacegraphically represents the estimated analyte data trend overpredetermined a time period (e.g., one, three, and nine hours,respectively). In alternative embodiments, other time periods can berepresented.

In some embodiments, the user interface begins displaying data to theuser after the sensor's stability has been affirmed. In some alternativeembodiments, however, the user interface displays data that is somewhatunstable (e.g., does not have sufficient stability and/or accuracy); inthese embodiments, the receiver may also include an indication ofinstability of the sensor data (e.g., flashing, faded, or anotherindication of sensor instability displayed on the user interface). Insome embodiments, the user interface informs the user of the status ofthe stability of the sensor data.

Accordingly, after initial calibration of the sensor, and optionallydetermination of stability of the sensor data, real-time continuousanalyte information can be displayed on the user interface so that theuser can regularly and proactively care for his/her diabetic conditionwithin the bounds set by his/her physician.

In alternative embodiments, the conversion function is used to predictanalyte values at future points in time. These predicted values can beused to alert the user of upcoming hypoglycemic or hyperglycemic events.Additionally, predicted values can be used to compensate for the timelag (e.g., 15 minute time lag such as described elsewhere herein), sothat an estimated analyte value displayed to the user represents theinstant time, rather than a time delayed estimated value.

In some embodiments, the substantially real time estimated analytevalue, a predicted future estimate analyte value, a rate of change,and/or a directional trend of the analyte concentration is used tocontrol the administration of a constituent to the user, including anappropriate amount and time, in order to control an aspect of the user'sbiological system. One such example is a closed loop glucose sensor andinsulin pump, wherein the analyte data (e.g., estimated glucose value,rate of change, and/or directional trend) from the glucose sensor isused to determine the amount of insulin, and time of administration,that may be given to a diabetic user to evade hyper-and hypoglycemicconditions.

In some embodiments, annotations are provided on the graph; for example,bitmap images are displayed thereon, which represent events experiencedby the host. For example, information about meals, insulin, exercise,sensor insertion, sleep, and the like, can be obtained by the receiver(by user input or receipt of a transmission from another device) anddisplayed on the graphical representation of the host's glucose overtime. It is believed that illustrating a host's life events matched witha host's glucose concentration over time can be helpful in educating thehost to his or her metabolic response to the various events.

In yet another alternative embodiment, the sensor utilizes one or moreadditional electrodes to measure an additional analyte. Suchmeasurements can provide a baseline or sensitivity measurement for usein calibrating the sensor. Furthermore, baseline and/or sensitivitymeasurements can be used to trigger events such as digital filtering ofdata or suspending display of data, all of which are described in moredetail in U.S. Patent Publication No. US-2005-0143635-A1.

Accordingly, after initial calibration of the sensor, continuous analytevalues can be displayed on the user interface so that the user canregularly and proactively care for his/her diabetic condition within thebounds set by his/her physician. Both the reference analyte data and thesensor analyte data from the continuous analyte sensor can be displayedto the user. In an embodiment wherein the continuous analyte sensorfunctions as an adjunctive device to a reference analyte monitor, theuser interface can display numeric reference analyte data, while showingthe sensor analyte data only in a graphical representation so that theuser can see the historical and present sensor trend information as wellas the most recent reference analyte data value. In an embodimentwherein the continuous analyte sensor functions as a non-adjunctivedevice to the reference analyte monitor, the user interface can displaythe reference analyte data and/or the sensor analyte data. The user cantoggle through menus and screens using the buttons in order to viewalternate data and/or screen formats, for example.

In alternative embodiments, the output module displays the estimatedanalyte values in a manner such as are described in more detail withreference to FIGS. 41 to 48, for example. In some embodiments, themeasured analyte value, an estimated future analyte value, a rate ofchange, and/or a directional trend of the analyte concentration is usedto control the administration of a constituent to the user, including anappropriate amount and time, in order to control an aspect of the user'sbiological system. One such example is a closed loop glucose sensor andinsulin pump, wherein the glucose data (for example, estimated glucosevalue, rate of change, and/or directional trend) from the glucose sensoris used to determine the amount of insulin, and time of administration,that can be given to a person with diabetes to evade hyperglycemic andhypoglycemic conditions. Output to external devices is described in moredetail with reference to FIGS. 53 to 36, for example.

FIG. 19A provides a flow chart 220 that illustrates a process which, forexample, a stability module can use in the evaluation of referenceand/or sensor data for stability, and/or for statistical, clinical,and/or physiological acceptability. Although some acceptability testsare disclosed herein, any known statistical, clinical, physiologicalstandards and methodologies can be applied to evaluate the acceptabilityof reference and sensor analyte data.

In some embodiments, a stability determination module is provided, alsoreferred to as the start-up module or processor module, which determinesthe stability of the analyte sensor over a period of time. Some analytesensors may have an initial instability time period during which theanalyte sensor is unstable for environmental, physiological, or otherreasons. Initial sensor instability can occur, for example, when theanalyte sensor is implanted subcutaneously; stabilization of the analytesensor can be dependent upon the maturity of the tissue ingrowth aroundand within the sensor. Initial sensor instability can also occur whenthe analyte sensor is implemented transdermally; stabilization of theanalyte sensor can be dependent upon electrode stabilization and/or thepresence of sweat, for example.

Accordingly, in some embodiments, achieving sensor stability can includewaiting a predetermined time period (e.g. an implantable subcutaneoussensor can require a time period for tissue growth, and a transcutaneoussensor can require time to equilibrate the sensor with the user's skin).In some embodiments, this predetermined waiting period for atranscutaneous sensor is from about one minute to about six days,preferably from about 1 day to about five days, and more preferably fromabout two days to about four days. In other embodiments, the waitingperiod for a transcutaneous sensor is preferably from about 30 minutesto about 24 hours, more preferably from about one hour to about 12hours, and most preferably from about 2 hours to about 10 hours. In someembodiments, this predetermined waiting period for a subcutaneous sensoris from about 1 day to about six weeks, preferably from about 1 week toabout five weeks, and more preferably about from two weeks to about fourweeks. In some embodiments, the sensitivity (e.g., sensor signalstrength with respect to analyte concentration) can be used to determinethe stability of the sensor; for example, amplitude and/or variabilityof sensor sensitivity can be evaluated to determine the stability of thesensor. In alternative embodiments, detection of pH levels, oxygen,hypochlorite, interfering species (e.g. ascorbate, urea, and/oracetaminophen), correlation between sensor and reference values (e.g.R-value), baseline drift and/or offset, and the like can be used todetermine the stability of the sensor. In one exemplary embodiment,wherein the sensor is a glucose sensor, a signal can be provided that isassociated with interfering species (e.g. ascorbate, urea, acetaminophenand/or the like), which can be used to evaluate sensor stability. Inanother exemplary embodiment, wherein the sensor is a glucose sensor,the counter electrode can be monitored for oxygen deprivation, which canbe used to evaluate sensor stability or functionality.

In some embodiments, the system (e.g. microprocessor) determines whetherthe analyte sensor is sufficiently stable according to certain criteria,such as are described above with reference to FIG. 18A. In oneembodiment wherein the sensor is an implantable glucose sensor, thesystem waits a predetermined time period for sufficient tissue ingrowthand evaluates the sensor sensitivity (e.g. from about one minute to sixweeks). In another embodiment, the receiver determines sufficientstability based on oxygen concentration near the sensor head. In yetanother embodiment, the sensor determines sufficient stability based ona reassessment of baseline drift and/or offset. In yet anotheralternative embodiment, the system evaluates stability by monitoring thefrequency content of the sensor data stream over a predetermined amountof time (e.g. 24 hours); in this alternative embodiment, a template (ortemplates) are provided that reflect acceptable levels of glucosephysiology and are compared with the actual sensor data, wherein apredetermined degree of agreement between the template and the actualsensor data is indicative of sensor stability. A few examples ofdeterminations of sufficient stability are described herein; however, avariety of known tests and parameters can be used to determine sensorstability without departing from the spirit and scope of the preferredembodiments. If the stability is determined to be insufficient,additional sensor data can be repeatedly taken at predeterminedintervals until a sufficient degree of stability is achieved.

In some embodiments, a clinical acceptability evaluation module, alsoreferred to as clinical module, evaluates the clinical acceptability ofnewly received reference data and/or time corresponding sensor data. Insome embodiments clinical acceptability criteria can include any of theconditions discussed above with reference to FIG. 18A as topre-screening or conditionally accepting reference analyte value data.In some embodiments of evaluating clinical acceptability, the rate ofchange of the reference data as compared to previously obtained data isassessed for clinical acceptability. That is, the rate of change andacceleration (or deceleration) of the concentration of many analytes invivo have certain physiological limits within the body. Accordingly, alimit can be set to determine if the new matched pair is within aphysiologically feasible range, indicated by a rate of change from theprevious data that is within known physiological and/or statisticallimits. Similarly, in some embodiments an algorithm that predicts afuture value of an analyte can be used to predict and then compare anactual value to a time corresponding predicted value to determine if theactual value falls within a clinically acceptable range based on thepredictive algorithm, for example.

In one exemplary embodiment, the clinical acceptability evaluationmodule matches the reference data with a substantially timecorresponding converted sensor value, and plots the matched data on aClarke Error Grid. Such a Clarke Error Grid is described in more detailwith reference to FIG. 19B, which is a graph of two data pairs on aClarke Error Grid that illustrates the evaluation of clinicalacceptability in one exemplary embodiment. The Clarke Error Grid can beused by the clinical acceptability evaluation module to evaluate theclinical acceptability of the disparity between a reference glucosevalue and a sensor glucose (e.g. estimated glucose) value, if any, in anembodiment wherein the sensor is a glucose sensor. The x-axis representsglucose reference glucose data and the y-axis represents estimatedglucose sensor data. Matched data pairs are plotted accordingly to theirreference and sensor values, respectively. In this embodiment, matchedpairs that fall within the A and B regions of the Clarke Error Grid areconsidered clinically acceptable, while matched pairs that fall withinthe C, D, and E regions of the Clarke Error Grid are not consideredclinically acceptable. Particularly, FIG. 19B shows a first matched pair1992 is shown which falls within the A region of the Clarke Error Grid,and therefore is considered clinically acceptable. A second matched pair1994 is shown which falls within the C region of the Clarke Error Grid,and therefore is not considered clinically acceptable.

A variety of other known methods of evaluating clinical acceptabilitycan be utilized. In one alternative embodiment, the Consensus Grid isused to evaluate the clinical acceptability of reference and sensordata. In another alternative embodiment, a mean absolute differencecalculation can be used to evaluate the clinical acceptability of thereference data. In another alternative embodiment, the clinicalacceptability can be evaluated using any relevant clinical acceptabilitytest, such as a known grid (e.g. Clarke Error or Consensus), and caninclude additional parameters such as time of day and/or an increasingor decreasing trend of the analyte concentration. In another alternativeembodiment, a rate of change calculation can be used to evaluateclinical acceptability. In yet another alternative embodiment, whereinthe reference data is received in substantially real time, theconversion function can be used to predict an estimated glucose value ata time corresponding to the time stamp of the reference analyte value(e.g. when there is a time lag of the sensor data such as describedelsewhere herein). Accordingly, a threshold can be set for the predictedestimated glucose value and the reference analyte value disparity, ifany.

The conventional analyte meters (e.g. self-monitored blood analytetests) are known to have a ±20% error in analyte values. Gross errors inanalyte readings are known to occur due to patient error inself-administration of the blood analyte test. For example, if the userhas traces of sugar on his/her finger while obtaining a blood sample fora glucose concentration test, then the measured glucose value is likelyto be much higher than the actual glucose value in the blood.Additionally, it is known that self-monitored analyte tests (e.g. teststrips) are occasionally subject to manufacturing defects.

Another cause for error includes infrequency and time delay that mayoccur if a user does not self-test regularly, or if a user self-testsregularly but does not enter the reference value at the appropriate timeor with the appropriate time stamp. Therefore, it can be advantageous tovalidate the acceptability of reference analyte values prior toaccepting them as valid entries. Accordingly, the receiver evaluates theclinical acceptability of received reference analyte data prior to theiracceptance as a valid reference value.

In one embodiment, the reference analyte data (and/or sensor analytedata) is evaluated with respect to substantially time correspondingsensor data (and/or substantially time corresponding reference analytedata) to determine the clinical acceptability of the reference analyteand/or sensor analyte data. A determination of clinical acceptabilityconsiders a deviation between time corresponding glucose measurements(e.g., data from a glucose sensor and data from a reference glucosemonitor) and the risk (e.g., to the decision making of a diabeticpatient) associated with that deviation based on the glucose valueindicated by the sensor and/or reference data. Evaluating the clinicalacceptability of reference and sensor analyte data, and controlling theuser interface dependent thereon, can minimize clinical risk.

In one embodiment, the receiver evaluates clinical acceptability eachtime reference data is obtained. In another embodiment, the receiverevaluates clinical acceptability after the initial calibration andstabilization of the sensor. In some embodiments, the receiver evaluatesclinical acceptability as an initial pre-screen of reference analytedata, for example after determining if the reference glucose measurementis between about 40 and 400 mg/dL. In other embodiments, other methodsof pre-screening data can be used, for example by determining if areference analyte data value is physiologically feasible based onprevious reference analyte data values (e.g., below a maximum rate ofchange).

In some embodiments, a calibration evaluation module evaluates the newmatched pair(s) for selective inclusion into the calibration set. Insome embodiments, the receiver simply adds the updated matched data pairinto the calibration set, displaces the oldest and/or least concordantmatched pair from the calibration set, and proceeds to recalculate theconversion function accordingly.

In some embodiments, the calibration evaluation includes evaluating onlythe new matched data pair. In some embodiments, the calibrationevaluation includes evaluating all of the matched data pairs in theexisting calibration set and including the new matched data pair; insuch embodiments not only is the new matched data pair evaluated forinclusion (or exclusion), but additionally each of the data pairs in thecalibration set are individually evaluated for inclusion (or exclusion).In some alternative embodiments, the calibration evaluation includesevaluating all possible combinations of matched data pairs from theexisting calibration set and including the new matched data pair todetermine which combination best meets the inclusion criteria. In someadditional alternative embodiments, the calibration evaluation includesa combination of at least two of the above-described evaluation method.

Inclusion criteria include at least one criterion that defines a set ofmatched data pairs that form a substantially optimal calibration set.Such criteria can include any of the conditions discussed above withreference to FIG. 18A concerning methods of pre-screening orconditionally accepting reference analyte value data. One inclusioncriterion involves the time stamp of the matched data pairs (that makeup the calibration set) spanning at least a predetermined time period(e.g. three hours). Another inclusion criterion involves the time stampsof the matched data pairs not being more than a predetermined age (e.g.one week old). Another inclusion criterion involves the matched pairs ofthe calibration set having a substantially evenly distributed amount ofhigh and low raw sensor data, estimated sensor analyte values, and/orreference analyte values. Another criterion involves all raw sensordata, estimated sensor analyte values, and/or reference analyte valuesbeing within a predetermined range (e.g. 40 to 400 mg/dL for glucosevalues). Another criterion involves a rate of change of the analyteconcentration (e.g. from sensor data) during the time stamp of thematched pair(s). For example, sensor and reference data obtained duringthe time when the analyte concentration is undergoing a slow rate ofchange is typically less susceptible to inaccuracies caused by time lagand other physiological and non-physiological effects. Another criterioninvolves the congruence of respective sensor and reference data in eachmatched data pair; the matched pairs with the most congruence arechosen. Another criterion involves physiological changes (e.g. lowoxygen due to a user's posture that may effect the function of asubcutaneously implantable analyte sensor) to ascertain a likelihood oferror in the sensor value. Evaluation of calibration set criteria caninvolve evaluating one, some, or all of the above described inclusioncriteria. It is contemplated that additional embodiments can compriseadditional inclusion criteria not explicitly described herein.

In some embodiments, a quality evaluation module evaluates the qualityof the calibration. In one embodiment, the quality of the calibration isbased on the association of the calibration set data using statisticalanalysis. Statistical analysis can include any known cost function, suchas linear regression, non-linear mapping/regression, rank (e.g.,non-parametric) correlation, least mean square fit, mean absolutedeviation (MAD), mean absolute relative difference, and the like. Theresult of the statistical analysis provides a measure of the associationof data used in calibrating the system. A threshold of data associationcan be set to determine if sufficient quality is exhibited in acalibration set.

In another embodiment, the quality of the calibration is determined byevaluating the calibration set for clinical acceptability, such as, forexample using a Clarke Error Grid, Consensus Grid, or clinicalacceptability test. As an example, the matched data pairs that form thecalibration set can be plotted on a Clarke Error Grid, such that whenall matched data pairs fall within the A and B regions of the ClarkeError Grid, then the calibration is determined to be clinicallyacceptable.

In yet another alternative embodiment, the quality of the calibration isdetermined based initially on the association of the calibration setdata using statistical analysis, and then by evaluating the calibrationset for clinical acceptability. If the calibration set fails thestatistical and/or the clinical test, the calibration processingrecalculates the conversion function with a new (e.g. optimized) set ofmatched data pairs. In this embodiment, the processing loop iteratesuntil the quality evaluation module: 1) determines clinicalacceptability; 2) determines sufficient statistical data association; 3)determines both clinical acceptability and sufficient statistical dataassociation; or 4) surpasses a threshold of iterations.

Calibration of analyte sensors can be variable over time; that is, theconversion function suitable for one point in time may not be suitablefor another point in time (e.g. hours, days, weeks, or months later).For example, in an embodiment wherein the analyte sensor issubcutaneously implantable, the maturation of tissue ingrowth over timecan cause variability in the calibration of the analyte sensor. Asanother example, physiological changes in the user (e.g. metabolism,interfering blood constituents, and lifestyle changes) can causevariability in the calibration of the sensor. Accordingly, acontinuously updating calibration algorithm that includes reforming thecalibration set, and thus recalculating the conversion function, overtime according to a set of inclusion criteria is advantageous.

One cause for discrepancies in reference and sensor data is asensitivity drift that can occur over time, when a sensor is insertedinto a host and cellular invasion of the sensor begins to blocktransport of the analyte to the sensor, for example. Therefore, it canbe advantageous to validate the acceptability of converted sensor dataagainst reference analyte data, to determine if a drift of sensitivityhas occurred and whether the calibration should be updated.

In one embodiment, the reference analyte data is evaluated with respectto substantially time corresponding converted sensor data to determinethe acceptability of the matched pair. For example, clinicalacceptability considers a deviation between time corresponding analytemeasurements (for example, data from a glucose sensor and data from areference glucose monitor) and the risk (for example, to the decisionmaking of a person with diabetes) associated with that deviation basedon the glucose value indicated by the sensor and/or reference data.Evaluating the clinical acceptability of reference and sensor analytedata, and controlling the user interface dependent thereon, can minimizeclinical risk. Preferably, the receiver evaluates clinical acceptabilityeach time reference data is obtained.

After initial calibration, such as is described in more detail withreference to FIG. 18, the sensor data receiving module 222 receivessubstantially continuous sensor data (e.g. a data stream) via a receiverand converts that data into estimated analyte values. As used herein,the term “substantially continuous” is a broad term and is used in itsordinary sense, without limitation, to refer to a data stream ofindividual measurements taken at time intervals (e.g. time-spaced)ranging from fractions of a second up to, e.g. 1, 2, or 5 minutes ormore. As sensor data is continuously converted, it can be occasionallyrecalibrated in response to changes in sensor sensitivity (drift), forexample. Initial calibration and re-calibration of the sensor require areference analyte value. Accordingly, the receiver can receive referenceanalyte data at any time for appropriate processing.

At block 222, the reference data receiving module, also referred to asthe reference input module, receives reference analyte data from areference analyte monitor. In one embodiment, the reference datacomprises one analyte value obtained from a reference monitor. In somealternative embodiments however, the reference data includes a set ofanalyte values entered by a user into the interface and averaged byknown methods, such as are described elsewhere herein. In somealternative embodiments, the reference data comprises a plurality ofanalyte values obtained from another continuous analyte sensor.

The reference data can be pre-screened according to environmental andphysiological issues, such as time of day, oxygen concentration,postural effects, and patient-entered environmental data. In oneexemplary embodiment, wherein the sensor comprises an implantableglucose sensor, an oxygen sensor within the glucose sensor is used todetermine if sufficient oxygen is being provided to successfullycomplete the necessary enzyme and electrochemical reactions for accurateglucose sensing. In another exemplary embodiment, the patient isprompted to enter data into the user interface, such as meal timesand/or amount of exercise, which can be used to determine likelihood ofacceptable reference data. In yet another exemplary embodiment, thereference data is matched with time-corresponding sensor data, which isthen evaluated on a modified clinical error grid to determine itsclinical acceptability.

Some evaluation data, such as described in the paragraph above, can beused to evaluate an optimum time for reference analyte measurement.Correspondingly, the user interface can then prompt the user to providea reference data point for calibration within a given time period.Consequently, because the receiver proactively prompts the user duringoptimum calibration times, the likelihood of error due to environmentaland physiological limitations can decrease and consistency andacceptability of the calibration can increase.

At block 224, the evaluation module, also referred to as acceptabilitymodule, evaluates newly received reference data. In one embodiment, theevaluation module evaluates the clinical acceptability of newly receivedreference data and time corresponding converted sensor data (new matcheddata pair). In one embodiment, a clinical acceptability evaluationmodule 224 matches the reference data with a substantially timecorresponding converted sensor value, and determines the Clarke ErrorGrid coordinates. In this embodiment, matched pairs that fall within theA and B regions of the Clarke Error Grid are considered clinicallyacceptable, while matched pairs that fall within the C, D, and E regionsof the Clarke Error Grid are not considered clinically acceptable.

A variety of other known methods of evaluating clinical acceptabilitycan be utilized. In one alternative embodiment, the Consensus Grid isused to evaluate the clinical acceptability of reference and sensordata. In another alternative embodiment, a mean absolute differencecalculation can be used to evaluate the clinical acceptability of thereference data. In another alternative embodiment, the clinicalacceptability can be evaluated using any relevant clinical acceptabilitytest, such as a known grid (e.g. Clarke Error or Consensus), andadditional parameters, such as time of day and/or the increase ordecreasing trend of the analyte concentration. In another alternativeembodiment, a rate of change calculation can be used to evaluateclinical acceptability. In yet another alternative embodiment, whereinthe received reference data is in substantially real time, theconversion function could be used to predict an estimated glucose valueat a time corresponding to the time stamp of the reference analyte value(this can be required due to a time lag of the sensor data such asdescribed elsewhere herein). Accordingly, a threshold can be set for thepredicted estimated glucose value and the reference analyte valuedisparity, if any. In some alternative embodiments, the reference datais evaluated for physiological and/or statistical acceptability asdescribed in more detail elsewhere herein.

At decision block 226, results of the evaluation are assessed. Ifacceptability is determined, then processing continues to block 228 tore-calculate the conversion function using the new matched data pair inthe calibration set.

At block 228, the conversion function module re-creates the conversionfunction using the new matched data pair associated with the newlyreceived reference data. In one embodiment, the conversion functionmodule adds the newly received reference data (e.g. including thematched sensor data) into the calibration set, and recalculates theconversion function accordingly. In alternative embodiments, theconversion function module displaces the oldest, and/or least concordantmatched data pair from the calibration set, and recalculates theconversion function accordingly.

At block 230, the sensor data transformation module uses the newconversion function (from block 228) to continually (or intermittently)convert sensor data into estimated analyte values, also referred to ascalibrated data, or converted sensor data, such as is described in moredetail above.

At block 232, an output module provides output to the user via the userinterface. The output is representative of the estimated analyte value,which is determined by converting the sensor data into a meaningfulanalyte value. User output can be in the form of a numeric estimatedanalyte value, an indication of directional trend of analyteconcentration, and/or a graphical representation of the estimatedanalyte data over a period of time, for example. Other representationsof the estimated analyte values are also possible, for example audio andtactile.

If, however, acceptability is determined at decision block 226 asnegative (unacceptable), then the processing progresses to block 234 toadjust the calibration set. In one embodiment of a calibration setadjustment, the conversion function module removes one or more oldestmatched data pair(s) and recalculates the conversion functionaccordingly. In an alternative embodiment, the conversion functionmodule removes the least concordant matched data pair from thecalibration set, and recalculates the conversion function accordingly.

At block 236, the conversion function module re-creates the conversionfunction using the adjusted calibration set. While not wishing to bebound by theory, it is believed that removing the least concordantand/or oldest matched data pair(s) from the calibration set can reduceor eliminate the effects of sensor sensitivity drift over time,adjusting the conversion function to better represent the currentsensitivity of the sensor.

At block 224, the evaluation module re-evaluates the acceptability ofnewly received reference data with time corresponding converted sensordata that has been converted using the new conversion function (block236). The flow continues to decision block 238 to assess the results ofthe evaluation, such as described with reference to decision block 226,above. If acceptability is determined, then processing continues toblock 230 to convert sensor data using the new conversion function andcontinuously display calibrated sensor data on the user interface.

If, however, acceptability is determined at decision block 226 asnegative, then the processing loops back to block 234 to adjust thecalibration set once again. This process can continue until thecalibration set is no longer sufficient for calibration, for example,when the calibration set includes only one or no matched data pairs withwhich to create a conversion function. In this situation, the system canreturn to the initial calibration or start-up mode, which is describedin more detail with reference to FIGS. 18 and 21, for example.Alternatively, the process can continue until inappropriate matched datapairs have been sufficiently purged and acceptability is positivelydetermined.

In alternative embodiments, the acceptability is determined by a qualityevaluation, for example, calibration quality can be evaluated bydetermining the statistical association of data that forms thecalibration set, which determines the confidence associated with theconversion function used in calibration and conversion of raw sensordata into estimated analyte values. See, e.g. U.S. Patent PublicationNo. US-2005-0027463-A1.

Alternatively, each matched data pair can be evaluated based on clinicalor statistical acceptability such as described above; however, when amatched data pair does not pass the evaluation criteria, the system canbe configured to ask for another matched data pair from the user. Inthis way, a secondary check can be used to determine whether the erroris more likely due to the reference glucose value or to the sensorvalue. If the second reference glucose value substantially correlates tothe first reference glucose value, it can be presumed that the referenceglucose value is more accurate and the sensor values are errant. Somereasons for errancy of the sensor values include a shift in the baselineof the signal or noise on the signal due to low oxygen, for example. Insuch cases, the system can be configured to re-initiate calibrationusing the secondary reference glucose value. If, however, the referenceglucose values do not substantially correlate, it can be presumed thatthe sensor glucose values are more accurate and the reference glucosevalues eliminated from the algorithm.

FIG. 20 provides is a flow chart 250 that illustrates the evaluation ofcalibrated sensor data for aberrant values in one embodiment. Althoughsensor data are typically accurate and reliable, it can be advantageousto perform a self-diagnostic check of the calibrated sensor data priorto displaying the analyte data on the user interface.

One reason for anomalies in calibrated sensor data includes transientevents, such as local ischemia at the implant site, which cantemporarily cause erroneous readings caused by insufficient oxygen toreact with the analyte. Accordingly, the flow chart 190 illustrates oneself-diagnostic check that can be used to catch erroneous data beforedisplaying it to the user.

At block 252, a sensor data receiving module, also referred to as thesensor data module, receives new sensor data from the sensor.

At block 24, the sensor data transformation module continuously (orintermittently) converts new sensor data into estimated analyte values,also referred to as calibrated data.

At block 256, a self-diagnostic module compares the new calibratedsensor data with previous calibrated sensor data, for example, the mostrecent calibrated sensor data value. In comparing the new and previoussensor data, a variety of parameters can be evaluated. In oneembodiment, the rate of change and/or acceleration (or deceleration) ofchange of various analytes, which have known physiological limits withinthe body, and sensor data can be evaluated accordingly. For example, alimit can be set to determine if the new sensor data is within aphysiologically feasible range, indicated by a rate of change from theprevious data that is within known physiological (and/or statistical)limits. Similarly, any algorithm that predicts a future value of ananalyte can be used to predict and then compare an actual value to atime corresponding predicted value to determine if the actual valuefalls within a statistically and/or clinically acceptable range based onthe predictive algorithm, for example. In certain embodiments,identifying a disparity between predicted and measured analyte data canbe used to identify a shift in signal baseline responsive to anevaluated difference between the predicted data and time-correspondingmeasured data. In some alternative embodiments, a shift in signalbaseline and/or sensitivity can be determined by monitoring a change inthe conversion function; namely, when a conversion function isre-calculated using the equation y=m×+b, a change in the values of m(sensitivity) or b (baseline) above a pre-selected “normal” threshold,can be used to trigger a fail-safe or further diagnostic evaluation.

Although the above-described self-diagnostics are generally employedwith calibrated sensor data, some alternative embodiments arecontemplated that check for aberrancy of consecutive sensor values priorto sensor calibration, for example, on the raw data stream and/or afterfiltering of the raw data stream. In certain embodiments, anintermittent or continuous signal-to-noise measurement can be evaluatedto determine aberrancy of sensor data responsive to a signal-to-noiseratio above a set threshold. In certain embodiments, signal residuals(e.g., by comparing raw and filtered data) can be intermittently orcontinuously analyzed for noise above a set threshold. In certainembodiments, pattern recognition can be used to identify noiseassociated with physiological conditions, such as low oxygen (see, e.g.U.S. Patent No. US-2005-0043598-A1), or other known signal aberrancies.Accordingly, in these embodiments, the system can be configured, inresponse to aberrancies in the data stream, to trigger signalestimation, adaptively filter the data stream according to theaberrancy, or the like, as described in more detail in the above citedU.S. Patent No. US-2005-0043598-A1.

In another embodiment, reference analyte values are processed todetermine a level of confidence, wherein reference analyte values arecompared to their time-corresponding calibrated sensor values andevaluated for clinical or statistical accuracy. In yet anotheralternative embodiment, new and previous reference analyte data arecompared in place of or in addition to sensor data. In general, thereexist known patterns and limitations of analyte values that can be usedto diagnose certain anomalies in raw or calibrated sensor and/orreference analyte data.

Block 193 describes additional systems and methods that can by utilizedby the self-diagnostics module of the preferred embodiments.

At decision block 258, the system determines whether the comparisonreturned aberrant values. In one embodiment, the slope (rate of change)between the new and previous sensor data is evaluated A change inconcentration value of greater than +/−10%, +/−15%, +/−20%, +/−25%, or+/−30%; and/or a rate of change of glucose concentration of +/−6mg/dL/min or more, preferably +/−5 or more, more preferably +/−4mg/dL/min or more, even more preferably +/−3 mg/dL/min or more, and mostpreferably +/−2 mg/dL/min or more are generally considered aberrant. Incertain embodiments, other known physiological parameters can be used todetermine aberrant values. However, a variety of comparisons andlimitations can be set.

At block 260, if the values are not found to be aberrant, the sensordata transformation module continuously (or intermittently) convertsreceived new sensor data into estimated analyte values, also referred toas calibrated data.

At block 262, if the values are found to be aberrant, the system goesinto a suspended mode, also referred to as fail-safe mode in someembodiments, which is described in more detail below with reference toFIG. 21. In general, suspended mode suspends display of calibratedsensor data and/or insertion of matched data pairs into the calibrationset. Preferably, the system remains in suspended mode until receivedsensor data is not found to be aberrant. In certain embodiments, a timelimit or threshold for suspension is set, after which system and/or userinteraction can be required, for example, requesting additionalreference analyte data, replacement of the electronics unit, and/orreset.

In some alternative embodiments, in response to a positive determinationof aberrant value(s), the system can be configured to estimate one ormore glucose values for the time period during which aberrant valuesexist. Signal estimation generally refers to filtering, data smoothing,augmenting, projecting, and/or other methods for estimating glucosevalues based on historical data, for example. In one implementation ofsignal estimation, physiologically feasible values are calculated basedon the most recent glucose data, and the aberrant values are replacedwith the closest physiologically feasible glucose values. See also U.S.Patent Publication No. US-2005-0027463-A1, U.S. Patent No.US-2005-0043598-A1, and U.S. Patent Publication No. US-2005-0203360-A1.

FIG. 21 provides a flow chart 280 that illustrates a self-diagnostic ofsensor data in one embodiment. Although reference analyte values canuseful for checking and calibrating sensor data, self-diagnosticcapabilities of the sensor provide for a fail-safe for displaying sensordata with confidence and enable minimal user interaction (for example,requiring reference analyte values only as needed).

At block 282, a sensor data receiving module, also referred to as thesensor data module, receives new sensor data from the sensor.

At block 284, the sensor data transformation module continuously (orintermittently) converts received new sensor data into estimated analytevalues, also referred to as calibrated data.

At block 286, a self-diagnostics module, also referred to as a fail-safemodule, performs one or more calculations to determine the accuracy,reliability, and/or clinical acceptability of the sensor data. Someexamples of the self-diagnostics module are described above, withreference block 256. The self-diagnostics module can be furtherconfigured to run periodically (e.g. intermittently or in response to atrigger), for example, on raw data, filtered data, calibrated data,predicted data, and the like.

In certain embodiments, the self-diagnostics module evaluates an amountof time since sensor insertion into the host, wherein a threshold is setfor the sensor's usable life, after which time period the sensor isconsidered to be unreliable. In certain embodiments, theself-diagnostics module counts the number of times a failure or reset isrequired (for example, how many times the system is forced intosuspended or start-up mode), wherein a count threshold is set for apredetermined time period, above which the system is considered to beunreliable. In certain embodiments, the self-diagnostics module comparesnewly received calibrated sensor data with previously calibrated sensordata for aberrant values, such as is described in more detail withreference to FIG. 5, above. In certain embodiments, the self-diagnosticsmodule evaluates clinical acceptability, such as is described in moredetail with reference to FIG. 20, above. In certain embodiments,diagnostics, such as are described in U.S. Pat. No. 7,081,195 and U.S.Patent Publication No. US-2005-0143635-A1 can be incorporated into thesystems of preferred embodiments for system diagnosis, for example, foridentifying interfering species on the sensor signal and for identifyingdrifts in baseline and sensitivity of the sensor signal.

In some embodiments, an interface control module, also referred to asthe fail-safe module, controls the user interface based upon theclinical acceptability of the reference data received. If the referencedata is not considered clinically acceptable, then a fail-safe modulebegins the initial stages of fail-safe mode. In some embodiments, theinitial stages of fail-safe mode include altering the user interface sothat estimated sensor data is not displayed to the user. In someembodiments, the initial stages of fail-safe mode include prompting theuser to repeat the reference analyte test and provide another referenceanalyte value. The repeated analyte value is then evaluated for clinicalacceptability.

If the results of the repeated analyte test are determined to beclinically unacceptable, then the fail-safe module can alter the userinterface to reflect full fail-safe mode. In one embodiment, fullfail-safe mode includes discontinuing sensor analyte display output onthe user interface. In other embodiments, color-coded information, trendinformation, directional information (e.g., arrows or angled lines),gauges, and/or other fail-safe information can be displayed, forexample.

The initial stages of fail-safe mode and full fail safe mode can includeuser interface control, for example. Additionally, it is contemplatedherein that a variety of different modes between initial and fullfail-safe mode can be provided, depending on the relative quality of thecalibration. In other words, the confidence level of the calibrationquality can control a plurality of different user interface screensproviding error bars, ±values, and the like. Similar screens can beimplemented in various clinical acceptability embodiments.

At block 288 of FIG. 21, a mode determination module, which can be apart of the sensor evaluation module 224, determines in which mode thesensor is set (or remains in). In some embodiments, the system isprogrammed with three modes: 1) start-up mode; 2) normal mode; and 3)suspended mode. Although three modes are described herein, the preferredembodiments are not limited to the number or types of modes with whichthe system can be programmed. In some embodiments, the system is definedas “in-cal” (in calibration) in normal mode; otherwise, the system isdefined as “out-of-cal’ (out of calibration) in start-up and suspendedmode. The terms as used herein are meant to describe the functionalityand are not limiting in their definitions.

Preferably, a start-up mode is provided wherein the start-up mode is setwhen the system determines that it can no longer remain in suspended ornormal mode (for example, due to problems detected by theself-diagnostics module, such as described in more detail above) and/orwhen the system is notified that a new sensor has been inserted. Uponinitialization of start-up mode, the system ensures that any old matcheddata pairs and/or calibration information is purged. In start-up mode,the system initializes the calibration set, such as is described in moredetail with reference to FIG. 14, above. Once the calibration set hasbeen initialized, sensor data is ready for conversion and the system isset to normal mode.

Preferably, a normal mode is provided wherein the normal mode is setwhen the system is accurately and reliably converting sensor data, forexample, wherein clinical acceptability is positively determined,aberrant values are negatively determined, and/or the self-diagnosticsmodules confirms reliability of data. In normal mode, the systemcontinuously (or intermittently) converts (or calibrates) sensor data.Additionally, reference analyte values received by the system arematched with sensor data points and added to the calibration set.

In certain embodiments, the calibration set is limited to apredetermined number of matched data pairs, after which the systemspurges old or less desirable matched data pairs when a new matched datapair is added to the calibration set. Less desirable matched data pairscan be determined by inclusion criteria, which include one or morecriteria that define a set of matched data pairs that form asubstantially optimal calibration set.

Unfortunately, some circumstances can exist wherein a system in normalmode is changed to start-up or suspended mode. In general, the system isprogrammed to change to suspended mode when a failure of clinicalacceptability, aberrant value check, and/or other self-diagnosticevaluation is determined, such as described in more detail above, andwherein the system requires further processing to determine whether asystem re-start is required (e.g. start-up mode). In general, the systemchanges to start-up mode when the system is unable to resolve itself insuspended mode and/or when the system detects that a new sensor has beeninserted (e.g. via system trigger or user input).

Preferably, a suspended mode is provided wherein the suspended mode isset when a failure of clinical acceptability, aberrant value check,and/or other self-diagnostic evaluation determines unreliability ofsensor data. In certain embodiments, the system enters suspended modewhen a predetermined time period passes without receiving a referenceanalyte value. In suspended mode, the calibration set is not updatedwith new matched data pairs, and sensor data can optionally beconverted, but not displayed on the user interface. The system can bechanged to normal mode upon resolution of a problem (positive evaluationof sensor reliability from the self-diagnostics module, for example).The system can be changed to start-up mode when the system is unable toresolve itself in suspended mode and/or when the system detects a newsensor has been inserted (via system trigger or user input).

The systems of preferred embodiments, including a transcutaneous analytesensor, mounting unit, electronics unit, applicator, and receiver forinserting the sensor, and measuring, processing, and displaying sensordata, provide improved convenience and accuracy because of theirdesigned stability within the host's tissue with minimum invasivetrauma, while providing a discreet and reliable data processing anddisplay, thereby increasing overall host comfort, confidence, safety,and convenience. Namely, the geometric configuration, sizing, andmaterial of the sensor of the preferred embodiments enable themanufacture and use of an atraumatic device for continuous measurementof analytes, in contrast to conventional continuous glucose sensorsavailable to persons with diabetes, for example. Additionally, thesensor systems of preferred embodiments provide a comfortable andreliable system for inserting a sensor and measuring an analyte levelfor up to 7 days or more without surgery. The sensor systems of thepreferred embodiments are designed for host comfort, with chemical andmechanical stability that provides measurement accuracy. Furthermore,the mounting unit is designed with a miniaturized and reusableelectronics unit that maintains a low profile during use. The usablelife of the sensor can be extended by incorporation of a bioactive agentinto the sensor that provides local release of an anti-inflammatory, forexample, in order to slow the subcutaneous foreign body response to thesensor.

After the usable life of the sensor (for example, due to a predeterminedexpiration, potential infection, or level of inflammation), the host canremove the transcutaneous sensor and mounting from the skin, and disposeof the sensor and mounting unit (preferably saving the electronics unitfor reuse). Another transcutaneous sensor system can be inserted withthe reusable electronics unit and thus provide continuous sensor outputfor long periods of time.

EXAMPLES

FIG. 22A is a graphical representation showing transcutaneous glucosesensor data and corresponding blood glucose values over time in a human.The x-axis represents time, the first y-axis represents current inpicoAmps, and the second y-axis represents blood glucose in mg/dL. Asdepicted on the legend, the small diamond points represent the currentmeasured from the working electrode of a transcutaneous glucose sensorof a preferred embodiment; while the larger points represent bloodglucose values of blood withdrawn from a finger stick and analyzed usingan in vitro self-monitoring blood glucose meter (SMBG).

A transcutaneous glucose sensor was built according to the preferredembodiments and implanted in a human host where it remained over aperiod of time. Namely, the sensor was built by providing a platinumwire, vapor-depositing the platinum with Parylene to form an insulatingcoating, helically winding a silver wire around the insulated platinumwire (to form a “twisted pair”), masking sections of the electroactivesurface of the silver wire, vapor-depositing Parylene on the twistedpair, chloridizing the silver electrode to form silver chloridereference electrode, and removing a radial window on the insulatedplatinum wire to expose a circumferential electroactive workingelectrode surface area thereon, this assembly also referred to as a“parylene-coated twisted pair assembly.”

An interference domain was formed on the parylene-coated twisted pairassembly by dip coating in an interference domain solution (7 weightpercent of a 50,000 molecular weight cellulose acetate (Sigma-Aldrich,St. Louis, Mo.) in a 2:1 acetone/ethanol solvent solution), followed bydrying at room temperature for 3 minutes. This interference domainsolution dip coating step was repeated two more times to form aninterference domain comprised of 3 layers of cellulose acetate on theassembly. The dip length (insertion depth) was adjusted to ensure thatthe cellulose acetate covered from the tip of the working electrode,over the exposed electroactive working electrode window, to cover adistal portion of the exposed electroactive reference electrode.

An enzyme domain was formed over the interference domain by subsequentlydip coating the assembly in an enzyme domain solution and drying in avacuum oven for 20 minutes at 50° C. This dip coating process wasrepeated once more to form an enzyme domain having two layers. Aresistance domain was formed over the interference domain bysubsequently spray coating the assembly with a resistance domainsolution and drying the assembly in a vacuum oven for 60 minutes at 50°C. Additionally, the sensors were exposed to electron beam radiation ata dose of 25 kGy, while others (control sensors) were not exposed toelectron beam radiation.

The graph of FIG. 22A illustrates approximately 3 days of data obtainedby the electronics unit operably connected to the sensor implanted inthe human host. Finger-prick blood samples were taken periodically andglucose concentration measured by a blood glucose meter (SMBG). Thegraphs show the subcutaneous sensor data obtained by the transcutaneousglucose sensor tracking glucose concentration as it rose and fell overtime. The time-corresponding blood glucose values show the correlationof the sensor data to the blood glucose data, indicating appropriatetracking of glucose concentration over time.

The raw data signal obtained from the sensor electronics has a currentmeasurement in the picoAmp range. Namely, for every unit (mg/dL) ofglucose, approximately 3.5 pA or less to 7.5 pA or more current ismeasured. Generally, the approximately 3.5 to 7.5 pA/mg/dL sensitivityexhibited by the device can be attributed to a variety of designfactors, including resistance of the membrane system to glucose, amountof enzyme in the membrane system, surface area of the working electrode,and electronic circuitry design. Accordingly, a current in the picoAmprange enables operation of an analyte sensor that: 1) requires (orutilizes) less enzyme (e.g. because the membrane system is highlyresistive and allows less glucose through for reaction in the enzymedomain); 2) requires less oxygen (e.g. because less reaction of glucosein the enzyme domain requires less oxygen as a co-reactant) andtherefore performs better during transient ischemia of the subcutaneoustissue; and 3) accurately measures glucose even in hypoglycemic ranges(e.g. because the electronic circuitry is able to measure very smallamounts of glucose (hydrogen peroxide at the working electrode)).Advantageously, the analyte sensors of the preferred embodiments exhibitimproved performance over convention analyte sensors at least in partbecause a current in the picoAmp range enables operation in conditionsof less enzyme, and less oxygen, better resolution, lower power usage,and therefore better performance in the hypoglycemic range wherein lowermg/dL values conventionally have yielded lower accuracy.

FIG. 22B is a graphical representation showing transcutaneous glucosesensor data and corresponding blood glucose values over time in a human.The x-axis represents time; the y-axis represents glucose concentrationin mg/dL. As depicted on the legend, the small diamond points representthe calibrated glucose data measured from a transcutaneous glucosesensor of a preferred embodiment; while the larger points representblood glucose values of blood withdrawn from a finger stick and analyzedusing an in vitro self-monitoring blood glucose meter (SMBG). Thecalibrated glucose data corresponds to the data of FIG. 22A shown incurrent, except it has been calibrated using algorithms of the preferredembodiments. Accordingly, accurate subcutaneous measurement of glucoseconcentration has been measured and processed using the systems andmethods of the preferred embodiments.

FIG. 23 is a graphical representation showing transcutaneous glucosesensor data and corresponding blood glucose values obtained overapproximately seven days in a human. The x-axis represents time; they-axis represents glucose concentration in mg/dL. As depicted on thelegend, the small diamond points represent the calibrated glucose datameasured from a transcutaneous glucose sensor of a preferred embodiment;while the larger points represent blood glucose values of bloodwithdrawn from a finger stick and analyzed using an in vitroself-monitoring blood glucose meter (SMBG). The calibrated glucose datacorresponds to a sensor that was implanted in a human for approximatelyseven days, showing an extended functional life, as compare to threedays, for example.

Differentiation of Sensor Systems

Some embodiments provide sensor systems suitable for implantation for 1,3, 5, 7, or 10 days or more. Alternatively, sensors designed for shorteror longer durations can have one or more specific design features (e.g.membrane systems, bioactive agent(s), architecture, electronic design,power source, software, or the like) customized for the intended sensorlife. Similarly, some embodiments provide sensor systems suitable for avariety of uses such as pediatrics, adults, geriatrics, persons withtype-1 diabetes, persons with type-2 diabetes, intensive care (ICU),hospital use, home use, rugged wear, everyday wear, exercise, and thelike, wherein the sensor systems include particular design features(e.g. membrane systems, bioactive agent(s), architecture, electronicdesign, power source, software, or the like) customized for an intendeduse. Accordingly, it can be advantageous to differentiate sensor systemsthat are substantially similar, for example, sensors wherein theelectronics unit of a sensor system can releasably mate with differentmounting units, or sensors wherein different electronics units designedfor different functionality can mate with a specific mounting unit.

In some embodiments, the mechanical, electrical, and/or software designenables the differentiation (e.g. non-interchangeability) of thesedifferent sensor systems. In other words, the sensor systems can be“keyed” to ensure a proper match between an electronics unit and amounting unit (housing including sensor) as described herein. The terms“key” and “keyed” as used herein are broad terms and are used in theirordinary sense, including, without limitation, to refer to systems andmethods that control the operable connection or operable communicationbetween the sensor, its associated electronics, the receiver, and/or itsassociated electronics. The terms are broad enough to includemechanical, electrical, and software “keys.” For example, a mechanicallydesigned key can include a mechanical design that allows an operableconnection between two parts, for example, a mating between theelectronics unit and the mounting unit wherein the contacts are keyed tomutually engage contacts of complementary parts. As another example, anelectronically designed key can include a radio frequency identificationchip (RFID chip) on the mounting unit, wherein the electronics unit isprogrammed to identify a predetermined identification number (key) fromthe RFID chip prior to operable connection or communication between thesensor and/or sensor electronics. As yet another example, a software keycan include a code or serial number that identifies a sensor and/orelectronics unit.

Accordingly, systems and methods are provided for measuring an analytein a host, including: a sensor configured for transcutaneous insertioninto a host's tissue; a housing adapted for placement external to thehost's tissue and for supporting the sensor; and an electronics unitreleasably attachable to said housing, wherein at least one of thehousing and the electronics unit are keyed to provide a match betweenthe sensor and the electronics unit.

In some embodiments, the housing (including a sensor) and its matchingelectronics unit(s) are keyed by a configuration of the one or morecontacts thereon. FIGS. 4A to 4C illustrate three unique contactconfigurations, wherein the configurations are differentiated by adistance between the first and second contacts located within thehousing. In this embodiment, a properly keyed electronics unit isconfigured with contacts that mate with the contacts on a mating housing(FIGS. 4A to 4C), for example a narrow contact configuration on ahousing mates only with a narrow contact configuration on an electronicsunit. Accordingly, in practice, only an electronics unit comprising acontact configuration that is designed for mutual engagement with asimilarly “keyed” housing can be operably connected thereto.

In some embodiments, the electronics unit is programmed with an ID,hereinafter referred to as a “transmitter ID,” that uniquely identifiesa sensor system. In one exemplary embodiment, wherein a first sensorsystem is designed for 3-day use and a second sensor system is designedfor 7-day use, the transmitter ID can be programmed to begin with a “3”or a “7” in order to differentiate the sensor systems. In practice, a3-day sensor system is programmed for 3-day use (see enforcement ofsensor expiration described in more detail below), and thus uponoperable connection of a 3-day sensor system, the receiver can functionfor the appropriate duration according to the transmitter ID.

In some embodiments, each sensor system is associated with a unique ornear-unique serial number, which is associated with one or a set ofsensor systems. This serial number can include information such asintended duration, calibration information, and the like, so that uponsensor insertion, and operable connection of the sensor electronics, theserial number can be manually entered into the receiver (from thepackaging, for example) or can be automatically transmitted from thesensor's electronics unit. In this way, the serial number can providethe necessary information to enable the sensor system to function forthe intended duration.

Additionally or alternatively, the electronics unit and/or mounting unitcan be labeled or coded, for example, alpha-numerically, pictorially, orcolorfully, to differentiate unique sensor systems. In this way, a useris less likely to confuse different sensor systems.

Enforcement of Sensor Expiration (Duration of Sensor Life)

In general, transcutaneous sensor systems can be designed for apredetermined life span (e.g. a few hours to a few days or more). Someembodiments provide sensor systems suitable for 1-, 3-, 5-, 7-or 10-daysor more. One potential problem that may occur in practice is thecontinued use of the sensor beyond its intended life; for example, ahost may not remove the sensor after its intended life and/or the hostcan detach and reattach the electronics unit into the mounting unit(which may cause a refresh of the sensor system and/or use beyond itsintended life in some circumstances). Accordingly, systems and methodsare needed for ensuring the sensor system is used for its properduration and that accidental or intentional efforts to improperly extendor reuse the sensor system are avoided.

The preferred embodiments provide systems and methods for measuring ananalyte in a host, the system including: a sensor adapted fortranscutaneous insertion through the skin of a host; a housing adaptedfor placement adjacent to the host's skin and for supporting the sensorupon insertion through the skin; and an electronics unit operablyconnected to the housing, wherein the sensor system is configured toprevent use of the sensor (e.g. to render the sensor inoperative) beyonda predetermined time period.

In some embodiments, the sensor system is configured to destroy thesensor when the electronics unit is removed and/or after a predeterminedtime period has expired. In one exemplary embodiment, a loop of materialsurrounds a portion of the sensor and is configured to retract thesensor (from the host) when the electronics unit is removed from thehousing. In another embodiment, the sensor system is configured to cut,crimp, or otherwise destroy the sensor when the electronics unit isremoved from the housing.

In some embodiments, the sensor system is programmed to determinewhether to allow an initialization of a new sensor. For example, thereceiver can be programmed to require the sensor be disconnected priorto initiation of the receiver for an additional sensor system. In onesuch embodiment, the receiver can be programmed to look for a zero fromthe electronics unit, indicating the sensor has been disconnected, priorto allowing a new sensor to be initiated. This can help to ensure that auser actually removes the electronics unit (and/or sensor) prior toinitialization of a new sensor. In another such embodiment, sensorinsertion information can be programmed into the sensor electronics,such that the sensor insertion information is transmitted to thereceiver to allow initialization of a new sensor.

In some embodiments, the receiver software receives information from theelectronics unit (e.g., intended duration, transmitter ID, expirationdate, serial code, manufacture date, or the like) and is programmed toautomatically shut down after a predetermined time period (intendedduration) or sensor expiration, for example.

In some embodiments, the receiver is programmed to algorithmicallyidentify a new sensor insertion by looking for change in signalcharacteristic (e.g., a spike indicating break-in period, no change insensor count values during the first hour, or the like). If a user hasnot inserted a new sensor, then the continued use of an expired sensorcan be detected and can be used to trigger a shut down of the sensorand/or receiver.

In some embodiments, each sensor system is associated with a unique ornear-unique serial number, which is associated with one or a set ofsensor systems as described in more detail above. In general, the serialnumber can include information such as calibration information, intendedduration, manufacture date, expiration date, and the like. For example,the serial number can provide sensor life (intended duration)information, which can be used to shut down the sensor and/or receiverafter the intended sensor life.

The above described systems and methods for differentiating sensorsystems and enforcing sensor lifetimes can be used alone or incombination, and can be combined with any of the preferred embodiments.

Dynamic and Intelligent Analyte Value Estimation

Estimative algorithms can be applied continuously, or selectively turnedon/off based on conditions. Conventionally, a data stream received froma continuous analyte sensor can provide an analyte value and output thesame to the host, which can be used to warn a patient or doctor ofexisting clinical risk. Conventionally, a data stream received from ananalyte sensor can provide historical trend analyte values, which can beused to educate a patient or doctor of individual historical trends ofthe patient's analyte concentration. However, the data stream cannot,without additional processing, provide future analyte values, which canbe useful in preventing clinically risky analyte values, compensatingfor time lag, and ensuring proper matching of sensor and referenceanalyte, for example such as described below. Timelier reporting ofanalyte values and prevention of clinically risky analyte values, forexample, prevention of hyper-and hypoglycemic conditions in a personwith diabetes, can decrease health complications that can result fromclinically risky situations.

FIG. 24 is a flow chart that illustrates the process 354 of estimatinganalyte values and outputting estimated analyte values in oneembodiment. In this embodiment, estimation is used to calculate analytedata for time during which no data exists (for example, data gaps orfuture data) or to replace data when large inaccuracies are believed toexist within data (for example, signal noise due to transient ischemia).Estimation of analyte values can be performed instead of, or incombination with, calibration of measured analyte values.

The estimating analyte values process 354 can be applied continuously,or selectively turned on/off based on conditions. The determination ofwhen to apply estimative algorithms is discussed in more detail below.In some embodiments, estimation can be applied only during approachingclinical risk to warn a patient or doctor in an effort to avoid theclinical risk, for example when the measured glucose concentration isoutside of a clinically acceptable threshold (for example, 100 to 200mg/dL) and/or the glucose concentration is increasing or decreasing at acertain rate of change (for example, 3 mg/dL/min), such as described inmore detail with reference to FIG. 25, for example. In some embodimentsestimation can be applied continuously, dynamically, or intermittentlyto compensate for a time lag associated with the analyte sensor, whichtime lag can be consistent, dynamic, and/or intermittent, such asdescribed in more detail below with reference to FIGS. 26 to 27, forexample. In some embodiments, estimation can be applied to aid indynamically and intelligently matching reference data with correspondingsensor data to ensure accurate outlier detection and/or calibration ofsensor data with reference data, such as described in more detail withreference to FIGS. 28 and 29, for example. In some embodiments,estimation can be applied continuously (or intermittently) in order toprovide analyte data that encourages more timely proactive behavior inpreempting clinical risk.

At a block 356, the estimate analyte values process 354 obtains sensordata, which can be raw, smoothed, and/or otherwise processed. In someembodiments, estimation can be applied to a raw data stream receivedfrom an analyte sensor, such as described in more detail elsewhereherein. In some embodiments, estimation can be applied to calibrateddata, such as described in more detail elsewhere herein.

At a block 358, the estimate analyte values process 354 dynamically andintelligently estimates analyte values based on measured analyte valuesusing estimative algorithms. In some embodiments, dynamic andintelligent estimation includes selecting an algorithm from a pluralityof algorithms to determine an estimative algorithm (for example, firstor second order regression) that best fits the present measured analytevalues, such as described in more detail with reference to FIGS. 30 and31, for example. In some embodiments, dynamic and intelligent estimationfurther includes constraining and/or expanding estimated analyte valuesusing physiological parameters, such as described in more detail withreference to FIGS. 32 and 33, for example. In some embodiments, dynamicand intelligent estimation further includes evaluating the selectedestimative algorithms, for example using a data association function,such as described in more detail with reference to FIGS. 30, 31, 34, and35. In some embodiments, dynamic and intelligent estimation includesanalyzing a possible variation associated with the estimated analytevalues, for example using statistical, clinical, or physiologicalvariations, such as described in more detail with reference to FIGS. 36to 38. In some embodiments, dynamic and intelligent estimation includescomparing previously estimated analyte values with measured analytevalues for a corresponding time period, determining the deviation, suchas described with reference to FIGS. 39 and 40, for example. In someembodiments, the resulting deviation from the comparison can be used todetermine a variation for future estimated analyte values. In someembodiments, the resulting deviation from the comparison can be used todetermine a confidence level in the estimative algorithms. In someembodiments, the resulting deviation from the comparison can be used toshow evidence of the benefits of displaying estimated analyte values onpatient behavior, namely how well the patient responds to the estimatedanalyte values and alters his/her behavior in order to better controlanalyte levels.

At a block 360, the output module 178 provides output to the userinterface 160 and/or the external device 180. In some embodiments,output of estimated analyte values is combined with output of measuredanalyte values, such as in more detail elsewhere herein, for examplecombined on an LCD screen, or by toggling between screens. In someembodiments, a target analyte value or range of analyte values is outputto the user interface alone, or in combination with the estimatedanalyte values, in order to provide a goal towards which the user canaim, such as described with reference to FIGS. 43 to 45, for example. Insome embodiments, an approaching clinical risk is output in the form ofa visual, audible, or tactile prompt, such as described with referenceto FIGS. 41 to 43, for example. In some embodiments, therapyrecommendations are output to aid the user in determining correctiveaction that can be performed in an effort to avoid or minimize clinicalrisk such as described with reference to FIG. 45, for example. In someembodiments, a visual representation of possible variations of theestimated analyte values, which variation can be due to statistical,clinical, or physiological considerations, such as described withreference to FIGS. 45 to 47, for example. In some embodiments, theoutput prompts a user to obtain a reference analyte value (not shown).In some embodiments, output is sent to an external device such asdescribed with reference to FIGS. 48 to 51, for example.

FIG. 25 is a graph that illustrates one embodiment, wherein estimationis triggered by an event such as a patient's blood glucose concentrationrising above a predetermined threshold (for example, 180 mg/dL). Thex-axis represents time in minutes; the y-axis represents glucoseconcentration in mg/dL. The graph shows an analyte trend graph,particularly, the graph shows measured glucose data 362 for about 90minutes up to time (t)=0. In this embodiment, the measured glucose data362 has been smoothed and calibrated, however smoothing and/orcalibrating may not be required in some embodiments. At t=0, estimationof the preferred embodiments is invoked and 15-minute estimated glucosedata 364 indicates that the glucose concentration will likely rise above220 mg/dL. The estimated glucose data 364 can be useful in providingalarms (e.g., hyper-and hypoglycemic alerts) and/or displaying on theuser interface of the receiver, for example. Alarms may not requireestimative algorithms in some embodiments, for example when zero, first,and/or second order calculations can be made to dynamically assess thestatic value, rate of change, and/or rate of acceleration of the analytedata in some embodiment.

In some embodiments, estimative algorithms are selectively applied whenthe reference and/or sensor analyte data indicates that the analyteconcentration is approaching clinical risk. The concentration of theanalyte values, the rate of change of the analyte values, and/or theacceleration of the analyte values can provide information indicative ofapproaching clinical risk. In an example wherein the analyte sensor is aglucose sensor, thresholds (for example, 100 to 200 mg/dL) can be setthat selectively turn on estimative algorithms that then dynamically andintelligently estimate upcoming glucose values, and optionally possiblevariations of those estimated glucose values, to appropriately forewarnof an upcoming patient clinical risk (for example, hypo-orhyperglycemia). Additionally, the rate of change and/or acceleration canbe considered to more intelligently turn on and calculate necessaryestimation and for alarms (e.g., hyper-and hypoglycemic alarms). Forexample, if a person with diabetes has a glucose concentration of 100mg/dL, but is trending upwardly, has slow or no rate of change, or isdecelerating downwardly, estimation and/or alarms may not be necessary.

FIG. 26 is a graph that illustrates a raw data stream and thecorresponding reference analyte values. The x-axis represents time inminutes, the first y-axis represents sensor glucose data measured incounts, and the second y-axis represents reference glucose data inmg/dL. A raw data stream 366 was obtained for a host from a continuousglucose sensor over a 4-hour time period. In this example, the raw datastream 366 has not been smoothed, calibrated, or otherwise processed andis represented in counts. Reference glucose values 368 were obtainedfrom the host using a reference glucose monitor during the same 4-hourtime period. The raw data stream 366 and reference glucose values 368were plotted on the graph of FIG. 26 accordingly during the 4-hour timeperiod. While not wishing to be bound by theory, the visible differencebetween the reference and sensor glucose data is believed to be causedat least in part by a time lag, such as described in more detail below.

A data stream received from an analyte sensor can include a time lagwithin the measured analyte concentration, for example, as compared tocorresponding reference analyte values. In some embodiments, a time lagcan be associated with a difference in measurement samples (for example,an interstitial fluid sample measured by an implantable analyte sensoras compared with a blood sample measured by an external referenceanalyte monitor). In some embodiments, a time lag can be associated withdiffusion of the analyte through a membrane system, for example such ashas been observed in some implantable electrochemically-based glucosesensors. Additionally in some embodiments, a time lag can be associatedwith processing of the data stream, for example, a finite impulseresponse filter (FIR) or infinite impulse response (IIR) filter can beapplied intermittently or continuously to a raw data stream in thesensor (or in the receiver) in order to algorithmically smooth the datastream, which can produce a time lag (for example, as shown in measuredglucose data 380 of FIG. 28). In some embodiments, wherein the analytesensor is a subcutaneously implantable sensor, there may be a variabletime lag associated with the tissue ingrowth at the biointerface at thetissue-device interface. Additionally, time lags can be variable upon ahost's metabolism. In some embodiments, a time lag of the referenceanalyte data may be associated with an amount of time a user takes totest and report a reference analyte value. Accordingly, the preferredembodiments provide for estimation of analyte values based on measuredanalyte values, which can be used to compensate for a time lag such asdescribed above, allow for output of analyte values that representestimated present analyte values without a time lag.

Accordingly, some embodiments selectively apply estimative algorithmsbased on a measured, estimated, or predetermined time lag associatedwith the continuous analyte sensor. In some embodiments, estimativealgorithms continuously run in order to continuously compensate for atime lag between reference and sensor data, such as described in moredetail below. In some embodiments, estimative algorithms run duringoutlier detection in order to intelligently and dynamically matchcorresponding reference and sensor data for more accurate outlierinclusion or exclusion, such as described in more detail below. In someembodiments, estimative algorithms run during matching of data pairs forconsideration in the calibration set in order to intelligently anddynamically match corresponding reference and sensor glucose data forbetter calibration, such as described in more detail below.

FIG. 27 is a flow chart that illustrates the process 370 of compensatingfor a time lag associated with a continuous analyte sensor to providereal-time estimated analyte data output in one embodiment. For thereasons described above, the system includes programming thatcontinuously or periodically (e.g., when a user activates the LCDscreen) compensates for a time lag in the system to provide a betterreal-time estimate to the user, for example.

At block 372, the time lag compensation process 370 obtains sensor data,which can be raw, smoothed, and/or otherwise processed. In someembodiments, estimation can be applied to a raw data stream receivedfrom an analyte sensor, such as described in more detail elsewhereherein. In some embodiments, estimation can be applied to calibrateddata, such as described in more detail elsewhere herein.

At block 374, the time lag compensation process 370 continuously orperiodically estimates analyte values for a present time period tocompensate for a physiological or computational time lag in the sensordata stream. For example, if a 20-minute time lag is known inherentwithin the continuous analyte sensor, the compensation can be a20-minute projected estimation to provide true present time (or “realtime”) analyte values. Some embodiments can continuously run estimationto compensate for time lag, while other embodiments can perform time lagcompensation estimation only when the user interface (e.g., LCD screen)is activated by a user. Known estimation algorithms and/or the dynamicand intelligent estimation algorithms of the preferred embodiments(e.g., such as described with reference to block 358 and FIGS. 30 to 40)can be used in estimating analyte values herein.

At block 376, the time lag compensation process 370 continuously orperiodically provides output of the present time estimated analytevalues, such as described in more detail above. Output can be sent tothe user interface 160 or to an external device 180.

Referring now to FIG. 28, which is a graph that illustrates the data ofFIG. 26, including reference analyte data, corresponding calibratedsensor analyte data, and corresponding estimated analyte data, showingcompensation for time lag using estimation. The x-axis represents timein minutes and the y-axis represents glucose concentration in mg/dL.Reference glucose values 368 were obtained from the host from thereference glucose monitor during the 4-hour time period and correspondto FIG. 26. Measured glucose data 380 was obtained by smoothing andcalibrating the raw data stream 366 of FIG. 26 using reference glucosevalues 368, such as described in more detail elsewhere herein. Estimatedglucose data 382 was obtained by estimating using dynamic andintelligent estimation of the preferred embodiments, which is describedin more detail below.

The measured glucose data 380 has been smoothed and thereby includes adata processing-related time lag, which may be in addition tophysiological or membrane-related time lag, for example. Therefore, themeasured glucose data 380 visibly lags behind the reference glucosevalues 368 on the graph. The estimated glucose data 382 includes dynamicand intelligent estimation of the preferred embodiments in order tocompensate for the time lag, thereby better correlating with thereference glucose values 368. In this embodiment, the time lagcompensation (estimation) is 15 minutes, however in other embodimentsthe time lag compensation (estimation) can be more or less.

In some embodiments, the estimation can be programmed to compensate fora predetermined time lag (for example, 0 to 60 minutes, or more). Insome alternative embodiments, the estimation can be dynamically adjustedbased on a measured time lag; for example, when estimation is used todynamically match sensor analyte data with reference analyte data suchas described below, the time difference between best correspondingsensor analyte data and reference analyte data can be used to determinethe time lag.

FIG. 29 is a flow chart that illustrates the process 384 of matchingdata pairs from a continuous analyte sensor and a reference analytesensor in one embodiment. Estimative algorithms of the preferredembodiments are useful when selectively applied during the process ofmatching corresponding sensor and reference analyte data, for exampleduring outlier detection and/or matching data pairs for calibration,such as described in more detail elsewhere herein. For the reasonsstated above with reference to FIGS. 26 to 28, for example, a time lagassociated with the continuous analyte sensor and/or the referenceanalyte monitor can hinder the ability to accurately match data from theanalyte sensor with corresponding data from the reference analytemonitor using time-correspondence only.

At block 386, the data matching process 384 obtains sensor data, whichcan be raw, smoothed, and/or otherwise processed. In some embodiments,data matching can use data from a raw data stream received from ananalyte sensor, such as described in more detail elsewhere herein. Insome embodiments, data matching can use calibrated data, such asdescribed in more detail elsewhere herein.

At block 388, the data matching process 384, receives analyte valuesfrom a reference analyte monitor, including one or more referenceglucose data points, hereinafter referred as “reference data” or“reference analyte data.” In an example wherein the analyte sensor is acontinuous glucose sensor, the reference analyte monitor can be aself-monitoring blood glucose (SMBG) meter. Other examples are describedin more detail elsewhere herein

At block 390, the data matching process 384 estimates one or moreanalyte values for a time period during which no data exists (or whendata is unreliable or inaccurate, for example) based on the data stream.For example, the estimated analyte values can include values atintervals from about 30 seconds to about 5 minutes, and can be estimatedfor a time period of about 5 minutes to about 60 minutes in the future.In some embodiments, the time interval and/or time period can be more orless. Known estimation algorithms and/or the dynamic and intelligentestimation algorithms of the preferred embodiments (e.g., such asdescribed with reference to block 358 and FIGS. 30 to 40) can be used inestimating analyte values herein.

At block 392, the data matching process 384 creates at least one matcheddata pair by matching reference analyte data to a corresponding analytevalue from the one or more estimated analyte values. In someembodiments, the best matched pair can be evaluated by comparing areference data point against individual sensor values over apredetermined time period (for example, +/−0 to 60 minutes). In one suchembodiment, the reference data point is matched with sensor data pointsat intervals (for example, 5-minute intervals of measured historicalanalyte values and estimated future analyte values) and each matchedpair is evaluated. The matched pair with the best correlation (forexample, based on statistical deviation, clinical risk analysis, or thelike) can be selected as the best matched pair and should be used fordata processing. In some alternative embodiments, matching a referencedata point with an average of a plurality of sensor data points over atime period can be used to form a matched pair.

Therefore, the preferred embodiments provide for estimation of analytevalues based on measured analyte values that can be helpful in moreaccurately and/or appropriately matching sensor and reference analytevalues that represent corresponding data. By increasing the accuracy ofmatched data pairs, true real-time estimated analyte values (forexample, without a time lag) can be provided, calibration can beimproved, and outlier detection can be more accurate and convenient,thereby improving overall patient safety and convenience.

While any of the above uses and applications can be applied usingconventional algorithms that provide conventional projection based onfirst or second order regression, for example, it has been found thatanalyte value estimation can be further improved by adaptively applyingalgorithms, for example using dynamic intelligence such as described inmore detail below. The dynamic and intelligent algorithms describedherein can be applied to the uses and applications described above, orfor estimating analyte values at any time for any use or application.

FIG. 30 is a flow chart that illustrates the dynamic and intelligentestimation algorithm selection process 396 in one embodiment.

At block 398, the dynamic and intelligent estimation algorithm selectionprocess 396 obtains sensor data, which can be raw, smoothed, and/orotherwise processed. In some embodiments, data matching can use datafrom a raw data stream received from an analyte sensor, such asdescribed in more detail elsewhere herein. In some embodiments, datamatching can use calibrated data, such as described in more detailelsewhere herein.

At block 400, the dynamic and intelligent estimation algorithm selectionprocess 396 includes selecting one or more algorithms from a pluralityof algorithms that best fits the measured analyte values. In someembodiments, the estimative algorithm can be selected based onphysiological parameters; for example, in an embodiment wherein theanalyte sensor is a glucose sensor, a first order regression can beselected when the rate of change of the glucose concentration is high,indicating correlation with a straight line, while a second orderregression can be selected when the rate of change of the glucoseconcentration is low, indicating correlation with a curved line. In someembodiments, a first order regression can be selected when the referenceglucose data is within a certain threshold (for example, 100 to 200mg/dL), indicating correlation with a straight line, while a secondorder regression can be selected when the reference glucose data isoutside of a certain threshold (for example, 100 to 200 mg/dL),indicating correlation with a curved line because the likelihood of theglucose concentration turning around (for example, having a curvature)is greatest at high and low values.

Generally, algorithms that estimate analyte values from measured analytevalues include any algorithm that fits the measured analyte values to apattern, and/or extrapolates estimated values for another time period(for example, for a future time period or for a time period during whichdata needs to be replaced). In some embodiments, a polynomial regression(for example, first order, second order, third order, etc.) can be usedto fit measured analyte values to a pattern, and then extrapolated. Insome embodiments, autoregressive algorithms (for example, IIR filter)can be used to fit measured analyte values to a pattern, and thenextrapolated. In some embodiments, measured analyte values can befiltered by frequency before projection (for example, by converting theanalyte values with a Fourier transform, filtering out high frequencynoise, and converting the frequency data back to time values by using aninverse Fourier transform); this data can then be projected forward(extrapolated) along lower frequencies. In some embodiments, measuredanalyte values can be represented with a Wavelet transform (for examplefiltering out specific noise depending on wavelet function), and thenextrapolate forward. In some alternative embodiments, computationalintelligence (for example, neural network-based mapping, fuzzy logicbased pattern matching, genetic-algorithms based pattern matching, orthe like) can be used to fit measured analyte values to a pattern,and/or extrapolate forward. In yet other alternative embodiments,time-series forecasting, using methods such as moving average (single ordouble), exponential smoothing (single, double, or triple), time seriesdecomposition, growth curves, Box-Jenkins, or the like. The plurality ofalgorithms of the preferred embodiments can utilize any one or more ofthe above-described algorithms, or equivalents, in order tointelligently select estimative algorithms and thereby estimate analytevalues.

In some embodiments, estimative algorithms further include parametersthat consider external influences, such as insulin therapy, carbohydrateconsumption, or the like. In one such example, these additionalparameters can be user input via the user interface 160 or transmittedfrom an external device 180, such as described in more detail withreference to FIG. 17A. By including such external influences inadditional to historical trend data (measured analyte values), analyteconcentration changes can be better anticipated.

At block 402, the selected one or more algorithms are evaluated based onstatistical, clinical, or physiological parameters. In some embodiments,running each algorithm on the data stream tests each of the one or morealgorithms, and the algorithmic result with the best correlation to themeasured analyte values is selected. In some embodiments, thepluralities of algorithms are each compared for best correlation withphysiological parameters (for example, within known or expected rates ofchange, acceleration, concentration, etc). In some embodiments, thepluralities of algorithms are each compared for best fit within aclinical error grid (for example, within “A” region of Clarke ErrorGrid). Although first and second order algorithms are exemplifiedherein, any two or more algorithms such as described in more detailbelow could be programmed and selectively used based on a variety ofconditions, including physiological, clinical, and/or statisticalparameters.

At block 404, the algorithm(s) selected from the evaluation step isemployed to estimate analyte values for a time period. Accordingly,analyte values are more dynamically and intelligently estimated toaccommodate the dynamic nature of physiological data. Additionalprocesses, for example applying physiological boundaries (FIG. 32),evaluation of the estimation algorithms after employing the algorithms(FIG. 34), evaluating a variation of estimated analyte values (FIG. 36),measuring and comparing analyte values (FIG. 39), or the like can beapplied to the dynamic and intelligent estimative algorithms describedwith reference to FIG. 30.

FIG. 31 is a graph that illustrates dynamic and intelligent estimationalgorithm selection applied to a data stream in one embodiment showingfirst order estimation, second order estimation, and the measuredglucose values for the time period, wherein the second order estimationshows a better correlation to the measured glucose data than the firstorder estimation. The x-axis represents time in minutes. The y-axisrepresents glucose concentration in mg/dL.

In the data of FIG. 31, measured (calibrated) sensor glucose data 406was obtained up to time t=0. At t=0, a first order regression 408 wasperformed on the measured data 406 to estimate the upcoming 15-minutetime period. A second order regression 410 was also performed on thedata to estimate the upcoming 15-minute time period. The intelligentestimation of the preferred embodiments, such as described in moredetail below, chose the second order regression 410 as the preferredalgorithm for estimation based on programmed conditions (at t=0). Thegraph of FIG. 31 further shows the measured glucose values 412 from t=0to t=15 to illustrate that second order regression 410 does in fact moreaccurately correlate with the measured glucose data 412 than first orderregression 408 from t=0 to t=15.

In the example of FIG. 31, the dynamic and intelligent estimationalgorithm selection determined that the second order regression 410 wasthe preferred algorithm for estimation at t=0 based on conditions. Afirst condition was based on a set threshold that considers second orderregression a better fit when measured glucose values are above 200 mg/dLand trending upwardly. A second condition verifies that the curvature ofthe second order regression line appropriately shows a decelerationabove 200 mg/dL. Although two specific examples of conditions aredescribed herein, dynamic and intelligent estimation can have as many oras few conditions programmed therein as can be imagined or contrived.Some additional examples of conditions for selecting from a plurality ofalgorithms are listed above, however the scope of this aspect of dynamicand intelligent estimation includes any conditional statements that canbe programmed and applied to any algorithms that can be implemented forestimation.

FIG. 32 is a flow chart that illustrates the process 414 of estimatinganalyte values within physiological boundaries in one embodiment. Theembodiment described herein is provided because the estimativealgorithms such as described with reference to FIG. 30 considermathematical equations, which may or may not be sufficient to accuratelyestimate analyte values based on measured analyte values.

At block 416, the analyte value estimation with physiological boundariesprocess 414 obtains sensor data, which can be raw, smoothed, calibratedand/or otherwise processed.

At block 418, the analyte value estimation with physiological boundariesprocess 414 estimates one or more analyte values using one or moreestimation algorithms. In some embodiments, this analyte valueestimation uses conventional projection using first or second orderregression, for example. In some embodiments, dynamically andintelligently selecting of one or more algorithms from a plurality ofalgorithms (FIG. 30), evaluating estimation algorithms after employingthe algorithms (FIG. 13), evaluating a variation of estimated analytevalues (FIG. 36), measuring and comparing analyte values (FIG. 39), orthe like can be applied to the dynamic and intelligent estimativealgorithms described with reference to FIG. 30.

At block 420, the analyte value estimation with physiological boundariesprocess 414 applies physiological boundaries to the estimated analytevalues of block 418. In some circumstances, physiological changes in ahost and associated sensor data stream follow a relatively mathematicalcurvature. However there are additional considerations that are notinherently included in the mathematical calculation of estimativealgorithms, such as physiological boundaries. One example of acircumstance or consideration that can occur is signal noise or signalartifact on the data stream, for example due to transient ischemia,signal from an interfering species, or the like. In such circumstances,normal mathematical calculations can result in estimated analyte valuesthat fall outside of physiological boundaries. For example, a firstorder regression can produce a line that exceeds a known physiologicalrate of change of glucose in humans (for example, about 4 to 5mg/dL/min). As another example, a second order regression can produce acurvature that exceeds a known physiological acceleration in humans (forexample, about 0.1 to 0.2 mg/dL/min²). As yet another example, it hasbeen observed that the best solution for the shape of the curve at anypoint along a glucose signal data stream over a certain time period (forexample, about 20 to 30 minutes) is a straight line, which can be usedto set physiological boundaries. As yet another example, a curvaturedefined by a second order regression at low glucose values (for example,below 80 mg/dL) generally decelerates as it goes down and accelerates asit goes up, while a curvature defined by a second order regression athigh glucose values generally decelerates as it goes up and acceleratesas it goes down. As yet another example, an individual's physiologicalpatterns can be monitored over a time period (for example, from aboutone day to about one year) and individual's physiological patternsquantified using pattern recognition algorithms; the individual'sphysiological patterns could be used to increase the intelligence of theestimation by applying the quantified patterns to the estimated analytevalues.

Accordingly, physiological boundaries, includes those described above,or other measured or known physiological analyte boundaries, cancompliment an estimative algorithm to ensure that the estimated analytevalues fall within known physiological parameters. However, in somealternative embodiments, physiological boundaries can be applied to rawand/or smoothed data, thereby eliminating the need for the estimationstep (block 418).

FIG. 33 is a graph that illustrates physiological boundaries applied toa data stream in one embodiment, wherein the dynamic and intelligentestimation includes performing an estimative algorithm and furtherapplies physiological boundaries to the estimated analyte data. Thex-axis represents time in minutes. The y-axis represents glucoseconcentration in mg/dL. Measured glucose data 422 is shown for about 90minutes up to t=0. At t=0, an estimative algorithm performs estimationusing a second order regression of the previous 40 minutes to generate aslope and acceleration, which are used to extrapolate the estimatedglucose data 424 beginning at the measured analyte data at t=0. At thesame time (t=0), the system uses known physiological parameters todetermine physiologically feasible boundaries of glucose concentrationover the estimated 15-minute period. In this example, the system uses aslope and intercept defined by a first order regression using 25 minutesof data up to t=0, from which the system sets physiological boundariesusing a maximum acceleration of glucose of 0.2 mg/dL/min² and a maximumrate of change of glucose of 4 mg/dL/min for the upcoming 15 minutes.Using the above-described physiological parameters, an upperphysiological boundary 426 and a lower physiological boundary 428 areset. Interestingly, the estimated glucose data 424 falls outside of thephysiological boundaries, namely above the upper physiological boundary426. In this case, the second order regression estimated glucose data424 has either a rate of change greater than 4 mg/dL/min and/oracceleration greater than 0.2 mg/dL/min². Such circumstances can becaused by noise on the signal, artifact of performing regression over apredetermined time period during which a change in analyte concentrationis not best described by a regression line, or numerous other suchaffects.

In this case, estimated glucose values 424 can be adjusted to be theupper limit 426 in order to better represent physiologically feasibleestimated analyte values. In some embodiments, some or all of theestimated analyte values falling outside of the physiological parameterscan trigger the dynamic and intelligent estimative algorithms tore-select an algorithm, or to adjust the parameters of the algorithm(for example, increase and/or decrease the number of data pointsconsidered by the algorithm) to better estimate during that time period.In some alternative embodiments, statistical and or clinical boundariescan be used to bound estimated analyte values and/or adjust theparameters that drive those algorithms.

FIG. 34 is a flow chart that illustrates the process 430 of dynamic andintelligent estimation and evaluation of analyte values in oneembodiment, wherein the estimation algorithms are continuously,periodically, or intermittently evaluated based on statistical,clinical, or physiological parameters to maintain accuracy ofestimation.

At block 432, the dynamic and intelligent estimation and evaluationprocess 430 obtains sensor data, which can be raw, smoothed, calibratedand/or otherwise processed.

At block 434, the dynamic and intelligent estimation and evaluationprocess 430 estimates one or more analyte values using one or moreestimation algorithms. In some embodiments, this analyte valueestimation uses conventional projection using first or second orderregression, for example. In some embodiments, dynamically andintelligently selecting of one or more algorithms from a plurality ofalgorithms (FIG. 30), dynamically and intelligently estimating analytevalues within physiological boundaries (FIG. 32), evaluating a variationof estimated analyte values (FIG. 36), measuring and comparing analytevalues (FIG. 39), or the like can be applied to the dynamic andintelligent estimation and evaluation process described herein withreference to FIG. 34.

The estimative algorithms described elsewhere herein considermathematical equations (FIG. 30) and optionally physiological parameters(FIG. 32), which may or may not be sufficient to accurately estimateanalyte values in some circumstances due to the dynamic nature ofmammalian behavior. For example, in a circumstance where a patient'sglucose concentration is trending upwardly at a constant rate of change(for example, 120 mg/dL at 2 mg/dL/min), an expected physiologicalpattern would likely estimate a continued increase at substantially thesame rate of change over the upcoming approximately 40 minutes, whichwould fall within physiological boundaries. However, if a person withdiabetes were to engage in heavy aerobic exercise, which may not beknown by the estimative algorithm, a slowing of the upward trend, andpossibly a change to a downward trend can possibly result, leading toinaccuracies in the estimated analyte values. Numerous suchcircumstances can occur in the lifestyle of a person with diabetes.However, although analyte values can sometimes be estimated under“normal” circumstances, other circumstances exist that are not “normal”or “expected” and can result in estimative algorithms that produceapparently erroneous results, for example, if they are based solely onmathematical calculations and/or physiological patterns. Accordingly,evaluation of the estimative algorithms can be performed to ensure theaccuracy or quantify a measure of confidence in the estimativealgorithms.

At block 436, the dynamic and intelligent estimation and evaluationprocess 430 evaluates the estimation algorithms employed at block 434 toevaluate a “goodness” of the estimated analyte values. The evaluationprocess performs an evaluation of the measured analyte data with thecorresponding estimated analyte data (e.g., by performing the algorithmon the data stream and comparing the measured with the correspondinganalyte data for a time period). In some embodiments, evaluation can beperformed continually or continuously so that the dynamic andintelligent algorithms are continuously adapting to the changingphysiological analyte data. In some embodiments, the evaluation can beperformed periodically so that the dynamic and intelligent algorithmsare periodically and systematically adapting to the changingphysiological analyte data. In some embodiments, evaluation can beperformed intermittently, for example when an estimative algorithm isinitiated or other such triggers, so that the dynamic and intelligentalgorithms can be evaluated when new or updated data or algorithms arebeing processed.

This evaluation process 430 uses any known evaluation method, forexample based on statistical, clinical, or physiological standards. Oneexample of statistical evaluation is provided below with reference toFIG. 35; however other methods are also possible. In some embodiments,the evaluation process 430 determines a correlation coefficient ofregression. In some embodiments wherein the sensor is a glucose sensor,the evaluation process 430 determines if the selected estimativealgorithm shows that analyte values fall with the “A” and “B” regions ofthe Clarke Error Grid. Other parameters or methods for evaluation areconsidered within the scope of the preferred embodiments. In someembodiments, the evaluation process 430 includes performing a curvatureformula to determine fiducial information about the curvature, whichresults in an evaluation of the amount of noise on the signal.

In some embodiments, the evaluation process 430 calculates physiologicalboundaries to evaluate whether the estimated analyte values fall withinknown physiological constraints. This evaluation is particularly helpfulwhen physiological constraints, such as described with reference to FIG.32 above, have not been applied to the estimative algorithm. In thisembodiment, the estimative algorithm(s) are evaluated to ensure thatthey do not allow estimated analyte values to fall outside ofphysiological boundaries, some examples of which are described in moredetail with reference to FIG. 32 above, and in the definitions section,for example. In some alternative embodiments, clinical or statisticalparameters can be used in a similar manner to bound estimated analytevalues.

If the result of the evaluation is satisfactory (for example, 10%average deviation, correlation coefficient above 0.79, all estimatedanalyte values within A or B region of the Clarke Error Grid, allestimated analyte values within physiological boundaries, or the like),the processing continues to the next step, using the selected estimativealgorithm. However, if the result of the evaluation is unsatisfactory,the process can start the algorithm selection process again, optionallyconsidering additional information, or the processor can determine thatestimation is not appropriate for a certain time period. In onealternative embodiment, a signal noise measurement can be evaluated, andif the signal to noise ratio is unacceptable, the processor can modifyits estimative algorithm or other action that can help compensate forsignal noise (e.g., signal artifacts, such as described in U.S. Pat. No.6,931,327, which is incorporated herein by reference in its entirety).

FIG. 35 is a graph that illustrates an evaluation of the selectedestimative algorithm in one embodiment, wherein a correlation ismeasured to determine a deviation of the measured glucose data with theselected estimative algorithm, if any. The x-axis represents time inminutes. The y-axis represents glucose concentration in mg/dL. Measuredglucose values 440 are shown for about 90 minutes up to t=0. At t=0, theselected algorithm is performed on 40 minutes of the measured glucosevalues 440 up to t=0, which is represented by a regression line 442 inthis embodiment. A data association function is used to determine agoodness of fit of the estimative algorithm on the measured glucose data440; namely, the estimative algorithm is performed retrospectively onthe measured glucose data 440, and is hereinafter referred to asretrospectively estimated glucose data 442 (e.g., estimation prior tot=0), after which a correlation (or deviation) with the measured glucosedata is determined. In this example, the goodness of fit shows a meanabsolute relative difference (MARD) of 3.3% between the measured glucosedata 440 and the retrospectively estimated glucose data 442. While notwishing to be bound to theory, it is believed that this correlation ofthe measured glucose data 440 to the retrospectively estimated glucosedata 442 can be indicative of the correlation of future estimatedglucose data to the measured glucose data for that estimated timeperiod.

Reference is now made to FIG. 36, which is a flow chart that illustratesthe process 450 of analyzing a variation of estimated future analytevalue possibilities in one embodiment. This embodiment takes intoconsideration that analyte values are subject to a variety of externalinfluences, which can cause the measured analyte values to alter fromthe estimated analyte values as the time period that was estimatedpasses. External influences include, but are not limited to, exercise,sickness, consumption of food and alcohol, injections of insulin, othermedications, or the like. For a person with diabetes, for example, evenwhen estimation does not accurately predict the upcoming measuredanalyte values, the estimated analyte values can be valuable to apatient in treatment and in fact can even alter the estimated path byencouraging proactive patient behavior that can cause the patient toavoid the estimated clinical risk. In other words, the deviation ofmeasured analyte values from their corresponding estimated analytevalues may not be an “error” in the estimative algorithm, and is in factone of the benefits of the continuous analyte sensor of the preferredembodiments, namely encouraging patient behavior modification andthereby improving patient health through minimizing clinically riskyanalyte values. Proactive behavior modification (for example, therapiessuch as insulin injections, carbohydrate consumption, exercise, or thelike) can cause the patient's measured glucose to change from theestimated path, and analyzing a variation that can be associated withthe estimated analyte values can encompass many of these changes.Therefore, in addition to estimated analyte values, a variation can becalculated or estimated based on statistical, clinical, and/orphysiological parameters that provides a range of values in which theestimated analyte values can fall.

At block 452, the variation of possible estimated analyte valuesanalysis process 450 obtains sensor data, which can be raw, smoothed,calibrated and/or otherwise processed.

At block 454, the variation of possible estimated analyte valuesanalysis process 450 estimates one or more analyte values using one ormore estimation algorithms. In some embodiments, this analyte valuesestimation uses conventional projection using first or second orderregression, for example. In some embodiments, dynamically andintelligently selecting of one or more algorithms from a plurality ofalgorithms (FIG. 30), dynamically and intelligently estimating analytevalues within physiological boundaries (FIG. 32), dynamic andintelligent estimation and evaluation of estimated analyte values (FIG.34), measuring and comparing analyte values (FIG. 39), or the like canbe applied to the dynamic and intelligent estimation and evaluationprocess described herein with reference to FIG. 36.

At block 456, the variation of possible estimated analyte valuesevaluation process 450 analyzes a variation of the estimated analytedata. Particularly, a statistical, clinical, and/or physiologicalvariation of estimated analyte values can be calculated when applyingthe estimative algorithms and/or can be calculated at regular intervalsto dynamically change as the measured analyte values are obtained. Ingeneral, analysis of trends and their variation allows the estimation ofthe preferred embodiments to dynamically and intelligently anticipateupcoming conditions, by considering internal and external influencesthat can affect analyte concentration.

In some embodiments, physiological boundaries for analytes in mammalscan be used to set the boundaries of variation. For example, knownphysiological boundaries of glucose in humans are discussed in detailherein, with reference to FIG. 32, and in the definitions section,however any physiological parameters for any measured analyte could beimplemented here to provide this variation of physiologically feasibleanalyte values.

In some embodiments, statistical variation can be used to determine avariation of possible analyte values. Statistical variation can be anyknown divergence or change from a point, line, or set of data based onstatistical information. Statistical information includes patterns ordata analysis resulting from experiments, published or unpublished, forexample. In some embodiments, statistical information can include normalpatterns that have been measured statistically in studies of analyteconcentrations in mammals, for example. In some embodiments, statisticalinformation can include errors observed and measured statistically instudies of analyte concentrations in mammals, for example. In someembodiments, statistical information can include predeterminedstatistical standards, for example, deviation less than or equal to 5%on the analyte value. In some embodiments, statistical variation can bea measured or otherwise known signal noise level.

In some embodiments, a variation is determined based on the fact thatthe conventional blood glucose meters are known to have up to a +/−20%error in glucose values (namely, on average in the hands of a patient).For example, gross errors in glucose readings are known to occur due topatient error in self-administration of the blood glucose test. In onesuch example, if the user has traces of sugar on his/her finger whileobtaining a blood sample for a glucose concentration test, then themeasured glucose value will likely be much higher than the measuredglucose value in the blood. Additionally, it is known thatself-monitored blood glucose tests (for example, test strips) areoccasionally subject to manufacturing error. In view of this statisticalinformation, in an embodiment wherein a continuous glucose sensor reliesupon a conventional blood glucose meter for calibration, this +/−20%error should be considered because of the potential for translatedeffect on the calibrated sensor analyte data. Accordingly, thisexemplary embodiment would provide for a +/−20% variation of estimatedglucose values based on the above-described statistical information.

In some embodiments, a variation of estimated analyte values can beanalyzed based on individual physiological patterns. Physiologicalpatterns are affected by a combination of at least biologicalmechanisms, physiological boundaries, and external influences such asexercise, sickness, consumption of food and alcohol, injections ofinsulin, other medications, or the like. Advantageously, patternrecognition can be used with continuous analyte sensors to characterizean individual's physiology; for example the metabolism of a person withdiabetes can be individually characterized, which has been difficult toquantify with conventional glucose sensing mechanisms due to the uniquenature of an individual's metabolism. Additionally, this information canbe advantageously linked with external influences (for example, patientbehavior) to better understand the nature of individual humanphysiology, which can be helpful in controlling the basal rate in aperson with diabetes, for example.

While not wishing to be bound to theory, it is believed that monitoringof individual historical physiological analyte data can be used torecognize patterns that can be used to estimate analyte values, orranges of values, in a mammal. For example, measured analyte data for apatient can show certain peaks of glucose levels during a specific timeof day, “normal” AM and PM eating behaviors (for example, that follow apattern), weekday versus weekend glucose patterns, individual maximumrate of change, or the like, that can be quantified usingpatient-dependent pattern recognition algorithms, for example. Patternrecognition algorithms that can be used in this embodiment include, butare not limited to, stochastic nonlinear time-series analysis,exponential (non-linear) autoregressive model, process feedbacknonlinear autoregressive (PFNAR) model, neural networks, or the like.

Accordingly, statistically calculated patterns can provide informationuseful in analyzing a variation of estimated analyte values for apatient that includes consideration of the patient's normalphysiological patterns. Pattern recognition enables the algorithmicanalysis of analyte data to be customized to a user, which is usefulwhen analyte information is variable with each individual user, such ashas been seen in glucose in humans, for example.

In some embodiments, a variation of estimated analyte values is onclinical risk analysis. Estimated analyte values can have higherclinical risk in certain ranges of analyte values, for example analytevalues that are in a clinically risky zone or analyte values that arechanging at a clinically risky rate of change. When a measured analytevalue or an estimated analyte value shows existing or approachingclinical risk, it can be important to analyze the variation of estimatedanalyte values in view of the clinical risk to the patient. For example,in an effort to aid a person with diabetes in avoiding clinically riskyhyper-or hypoglycemia, a variation can be weighted toward the clinicallyrisk zone, which can be used to emphasize the pending danger to thepatient, doctor, or care taker, for example. As another example, thevariation of measured or estimated analyte values can be based on valuesthat fall within the “A” and/or “B” regions of an error grid AnalysisMethod.

In case of variation analysis based on clinical risk, the estimatedanalyte values are weighted in view of pending clinical risk. Forexample, if estimated glucose values show a trend toward hypoglycemia ata certain rate of change, a variation of possible trends towardhypoglycemia are weighted to show how quickly the glucose concentrationcould reach 40 mg/dL, for example. As another example, if estimatedglucose values show a trend toward hyperglycemia at a certainacceleration, a variation of possible trends toward hyperglycemia areweighted to show how quickly the glucose concentration could reach 200mg/dL, for example.

In some embodiments, when a variation of the estimated analyte valuesshows higher clinical risk as a possible path within that variationanalysis as compared to the estimated analyte path, the estimatedanalyte values can be adjusted to show the analyte values with the mostclinical risk to a patient. While not wishing to be bound by theory,adjusting the estimated analyte values for the highest variation ofclinical risk exploits the belief that by showing the patient the “worstcase scenario,” the patient is more likely to address the clinical riskand make timely behavioral and therapeutic modifications and/ordecisions that will slow or reverse the approaching clinical risk.

At block 458, the variation of possible estimated analyte valuesevaluation process 450 provides output based on the variation analysis.In some embodiments, the result of this variation analysis provides a“zone” of possible values, which can be displayed to the user,considered in data analysis, and/or used in evaluating of performance ofthe estimation, for example. A few examples of variation analysisdisplay are shown in FIGS. 45 to 47; however other methods of formattingor displaying variation analysis data are contemplated within the scopeof the invention.

FIG. 37 is a graph that illustrates variation analysis of estimatedglucose values in one embodiment, wherein a variation of the estimatedglucose values is analyzed and determined based on known physiologicalparameters. The x-axis represents time in minutes. The y-axis representsglucose concentration in mg/dL. In this embodiment, the known maximumrate of change and acceleration of glucose in humans are used to providethe variation about the estimated glucose path.

The measured glucose values 460 are shown for about 90 minutes up tot=0. At t=0, intelligent and dynamic estimation of the preferredembodiments is performed to obtain estimated glucose values 462. Avariation of estimated glucose values is then determined based onphysiological parameters, including an upper limit 464 and a lower limit466 of variation defined by known physiological parameters, includingrate of change and acceleration of glucose concentration in humans.

FIG. 38 is a graph that illustrates variation of estimated analytevalues in another embodiment, wherein the variation is based onstatistical parameters. The x-axis represents time in minutes and they-axis represents glucose concentration in mg/dL. The measured glucosevalues 470 are shown for about 160 minutes up to t=0. At t=0,intelligent and dynamic estimation of the preferred embodiments isemployed to obtain estimated glucose values 472. A variation is definedby upper and lower limits 474 that were determined using 95% confidenceintervals. Bremer, T.; Gough, D. A. “Is blood glucose predictable fromprevious values? A solicitation for data.” Diabetes 1999, 48, 445-451,which is incorporated by reference herein in its entirety, teaches amethod of determining a confidence interval in one embodiment.

Although some embodiments have been described for a glucose sensor, anymeasured analyte pattern, data analysis resulting from an experiment, orotherwise known statistical information, whether official or unofficial,published or unpublished, proven or anecdotal, or the like, can be usedto provide the statistical variation described herein.

FIG. 39 is a flow chart that illustrates the process 480 of estimating,measuring, and comparing analyte values in one embodiment.

At block 482, the estimating, measuring, and comparing analyte valuesprocess 480 obtains sensor data, which can be raw, smoothed, calibratedand/or otherwise processed.

At block 484, the estimating, measuring, and comparing analyte valuesprocess 480 estimates one or more analyte values for a time period. Insome embodiments, this analyte values estimation uses conventionalprojection using first or second order regression, for example. In someembodiments, dynamically and intelligently selecting of one or morealgorithms from a plurality of algorithms (FIG. 30), dynamically andintelligently estimating analyte values within physiological boundaries(FIG. 32), dynamic and intelligent estimation and evaluation ofestimated analyte values (FIG. 34), variation analysis (FIG. 36), or thelike can be applied to the process described herein with reference toFIG. 39.

At block 486, the estimating, measuring, and comparing analyte valuesprocess 480 obtains sensor data for the time period for which theestimated analyte values were calculated at block 484. In someembodiments, the measured analyte data can be raw, smoothed, calibratedand/or otherwise processed.

At block 488, the estimating, measuring, and comparing analyte valuesprocess 480 compares the estimated analyte data to the measured analytedata for that estimated time period. In general, it can be useful tocompare the estimated analyte data to the measured analyte data for thatestimated time period after estimation of analyte values. Thiscomparison can be performed continuously, namely, at regular intervalsas data streams are processed into measured analyte values.Alternatively, this comparison can be performed based on events, such asduring estimation of measured analyte values, selection of a estimativealgorithm, evaluation of estimative algorithms, variation analysis ofestimated analyte values, calibration and transformation of sensoranalyte data, or the like.

One embodiment is shown in FIG. 40, wherein MARD is used to determine acorrelation (or deviation), if any, between the estimated and measureddata sets. In other embodiments, other methods, such as linearregression, non-linear mapping/regression, rank (for example,non-parametric) correlation, least mean square fit, mean absolutedeviation (MAD), or the like, can be used to compare the estimatedanalyte data to the measured analyte data to determine a correlation (ordeviation), if any.

In one embodiment, wherein estimation is used in outlier detectionand/or in matching data pairs for a continuous glucose sensor (see FIGS.27 and 28), the estimated glucose data can be plotted against referenceglucose data on a clinical error grid (for example, Clarke Error Grid orrate grid) and then compared to the measured glucose data for thatestimated time period plotted against the same reference analyte data onthe same clinical error grid. In alternative embodiments, other clinicalerror analysis methods can be used, such as Consensus Error Grid, rateof change calculation, consensus grid, and standard clinical acceptancetests, for example. The deviation can be quantified by percentdeviation, or can be classified as pass/fail, for example.

In some embodiments, the results of the comparison provide aquantitative deviation value, which can be used to provide a statisticalvariation; for example, if the % deviation is calculated as 8%, then thestatistical variation such as described with reference to FIG. 36 can beupdated with a +/−8% variation. In some alternative embodiments, theresults of the comparison can be used to turn on/off the estimativealgorithms, estimative output, or the like. In general, the comparisonproduces a confidence interval (for example, +/−8% of estimated values)which can be used in data analysis, output of data to a user, or thelike.

A resulting deviation from this comparison between estimated andcorresponding measured analyte values may or may not imply error in theestimative algorithms. While not wishing to be bound by theory, it isbelieved that the deviation between estimated and corresponding measuredanalyte values is due, at least in part, to behavioral changes by apatient, who observes estimated analyte values and determines to changethe present trend of analyte values by behavioral and/or therapeuticchanges (for example, medication, carbohydrate consumption, exercise,rest, or the like). Accordingly, the deviation can also be used toillustrate positive changes resulting from the educational aspect ofproviding estimated analyte values to the user, which is described inmore detail with reference to FIGS. 31 to 37.

FIG. 40 is a graph that illustrates comparison of estimated analytevalues in one embodiment, wherein previously estimated analyte valuesare compared to time corresponding measured analyte values to determinea correlation (or deviation), if any. The x-axis represents time inminutes. The y-axis represents glucose concentration in mg/dL. Themeasured glucose values 492 are shown for about 105 minutes up to t=15.The estimated analyte values 494, which were estimated at t=0 for 15minutes, are shown superimposed over the measured analyte values 492.Using a 3-point MARD for t=0 to t=15, the estimated analyte values 494can be compared with the measured analyte values 492 to determine a0.55% average deviation.

Input and Output

In general, the above-described estimative algorithms, includingestimation of measured analyte values and variation analysis of theestimated analyte values are useful when provided to a patient, doctor,family member, or the like. Even more, the estimative algorithms areuseful when they are able to provide information helpful in modifying apatient's behavior so that they experience less clinically riskysituations and higher quality of life than may otherwise be possible.Therefore, the above-described data analysis can be output in a varietyof forms useful in caring for the health of a patient.

Output can be provided via a user interface, including but not limitedto, visually on a screen, audibly through a speaker, or tactilelythrough a vibrator. Additionally, output can be provided via wired orwireless connection to an external device, including but not limited to,computer, laptop, server, personal digital assistant, modem connection,insulin delivery mechanism, medical device, or other device that can beuseful in interfacing with the receiver.

Output can be continuously provided, or certain output can beselectively provided based on events, analyte concentrations or thelike. For example, an estimated analyte path can be continuouslyprovided to a patient on an LCD screen, while audible alerts can beprovided only during a time of existing or approaching clinical risk toa patient. As another example, estimation can be provided based on eventtriggers (for example, when an analyte concentration is nearing orentering a clinically risky zone). As yet another example, analyzeddeviation of estimated analyte values can be provided when apredetermined level of variation (for example, due to known error orclinical risk) is known.

In contrast to alarms that prompt or alert a patient when a measured orprojected analyte value or rate of change simply passes a predeterminedthreshold, the clinical risk alarms of the preferred embodiments combineintelligent and dynamic estimative algorithms to provide greateraccuracy, more timeliness in pending danger, avoidance of false alarms,and less annoyance for the patient. In general, clinical risk alarms ofthe preferred embodiments include dynamic and intelligent estimativealgorithms based on analyte value, rate of change, acceleration,clinical risk, statistical probabilities, known physiologicalconstraints, and/or individual physiological patterns, thereby providingmore appropriate, clinically safe, and patient-friendly alarms.

In some embodiments, clinical risk alarms can be activated for apredetermined time period to allow for the user to attend to his/hercondition. Additionally, the clinical risk alarms can be de-activatedwhen leaving a clinical risk zone so as not to annoy the patient byrepeated clinical risk alarms, when the patient's condition isimproving.

In some embodiments, the dynamic and intelligent estimation of thepreferred embodiments determines a possibility of the patient avoidingclinical risk, based on the analyte concentration, the rate of change,and other aspects of the dynamic and intelligent estimative algorithmsof the preferred embodiments. If there is minimal or no possibility ofavoiding the clinical risk, a clinical risk alarm will be triggered.However, if there is a possibility of avoiding the clinical risk, thesystem can wait a predetermined amount of time and re-analyze thepossibility of avoiding the clinical risk. In some embodiments, whenthere is a possibility of avoiding the clinical risk, the system willfurther provide targets, therapy recommendations, or other informationthat can aid the patient in proactively avoiding the clinical risk.

In some embodiments, a variety of different display methods are used,such as described in the preferred embodiments, which can be toggledthrough or selectively displayed to the user based on conditions or byselecting a button, for example. As one example, a simple screen can benormally shown that provides an overview of analyte data, for examplepresent analyte value and directional trend. More complex screens canthen be selected when a user desired more detailed information, forexample, historical analyte data, alarms, clinical risk zones, or thelike.

FIG. 41 is an illustration of the receiver in one embodiment showing ananalyte trend graph, including measured analyte values, estimatedanalyte values, and a clinical risk zone. The receiver 158 includes anLCD screen 170, buttons 172, and a speaker 164 and/or microphone. Thescreen 170 displays a trend graph in the form of a line representing thehistorical trend of a patient's analyte concentration. Although axes mayor may not be shown on the screen 170, it is understood that atheoretical x-axis represents time and a theoretical y-axis representsanalyte concentration.

In some embodiments such as shown in FIG. 41, the screen showsthresholds, including a high threshold 500 and a low threshold 502,which represent boundaries between clinically safe and clinically riskyconditions for the patients. In one exemplary embodiment, a normalglucose threshold for a glucose sensor is set between about 100 and 160mg/dL, and the clinical risk zones 504 are illustrated outside of thesethresholds. In alternative embodiments, the normal glucose threshold isbetween about 80 and about 200 mg/dL, between about 55 and about 220mg/dL, or other threshold that can be set by the manufacturer,physician, patient, computer program, or the like. Although a fewexamples of glucose thresholds are given for a glucose sensor, thesetting of any analyte threshold is not limited by the preferredembodiments.

In some embodiments, the screen 170 shows clinical risk zones 504, alsoreferred to as danger zones, through shading, gradients, or othergraphical illustrations that indicate areas of increasing clinical risk.Clinical risk zones 504 can be set by a manufacturer, customized by adoctor, and/or set by a user via buttons 172, for example. In someembodiments, the danger zone 504 can be continuously shown on the screen170, or the danger zone can appear when the measured and/or estimatedanalyte values fall into the danger zone 504. Additional informationthat can be displayed on the screen includes, e.g., an estimated time toclinical risk. In some embodiments, the danger zone can be divided intolevels of danger (for example, low, medium, and high) and/or can becolor-coded (for example, yellow, orange, and red) or otherwiseillustrated to indicate the level of danger to the patient.Additionally, the screen or portion of the screen can dynamically changecolors or illustrations that represent a nearness to the clinical riskand/or a severity of clinical risk.

In some embodiments, such as shown in FIG. 41, the screen 170 displays atrend graph of measured analyte data 506. Measured analyte data can besmoothed and calibrated such as described in more detail elsewhereherein. Measured analyte data can be displayed for a certain time period(for example, previous 1 hour, 3 hours, 9 hours, etc.) In someembodiments, the user can toggle through screens using buttons 172 toview the measured analyte data for different time periods, usingdifferent formats, or to view certain analyte values (for example, highsand lows).

In some embodiments such as shown in FIG. 41, the screen 170 displaysestimated analyte data 508 using dots. In this illustration, the size ofthe dots can represent the confidence of the estimation, a variation ofestimated values, or the like. For example, as the time gets fartheraway from the present (t=0) the confidence level in the accuracy of theestimation can decline as is appreciated by one skilled in the art. Insome alternative embodiments, dashed lines, symbols, icons, or the likecan be used to represent the estimated analyte values. In somealternative embodiments, shaded regions, colors, patterns, or the likecan also be used to represent the estimated analyte values, a confidencein those values, and/or a variation of those values, such as describedin more detail in preferred embodiments.

Axes, including time and analyte concentration values, can be providedon the screen, however are not required. While not wishing to be boundby theory, it is believed that trend information, thresholds, and dangerzones provide sufficient information to represent analyte concentrationand clinically educate the user. In some embodiments, time can berepresented by symbols, such as a sun and moon to represent day andnight. In some embodiments, the present or most recent measured analyteconcentration, from the continuous sensor and/or from the referenceanalyte monitor can be continually, intermittently, or selectivelydisplayed on the screen.

The estimated analyte values 508 of FIG. 41 include a portion, whichextends into the danger zone 504. By providing data in a format thatemphasizes the possibility of clinical risk to the patient, appropriateaction can be taken by the user (for example, patient or caretaker) andclinical risk can be preempted.

FIG. 42 is an illustration of the receiver in another embodiment showinga representation of analyte concentration and directional trend using agradient bar. In this embodiment, the screen illustrates the measuredanalyte values and estimated analyte values in a simple but effectivemanner that communicates valuable analyte information to the user.

In this embodiment, a gradient bar 510 is provided that includesthresholds 512 set at high and lows such as described in more detailwith reference to FIG. 41, above. Additionally, colors, shading, orother graphical illustration can be present to represent danger zones514 on the gradient bar 510 such as described in more detail withreference to FIG. 41, above.

The measured analyte value is represented on the gradient bar 510 by amarker 516, such as a darkened or colored bar. By representing themeasured analyte value with a bar 516, a low-resolution analyte value ispresented to the user (for example, within a range of values). Forexample, each segment on the gradient bar 510 can represent about 10mg/dL of glucose concentration. As another example, each segment candynamically represent the range of values that fall within the “A” and“B” regions of the Clarke Error Grid. While not wishing to be bound bytheory, it is believe that inaccuracies known both in reference analytemonitors and/or continuous analyte sensors are likely due to knownvariables such as described in more detail elsewhere herein, and can bede-emphasized such that a user focuses on proactive care of thecondition, rather than inconsequential discrepancies within and betweenreference analyte monitors and continuous analyte sensors.

Additionally, the representative gradient bar communicates thedirectional trend of the analyte concentration to the user in a simpleand effective manner, namely by a directional arrow 518. For example, inconventional diabetic blood glucose monitoring, a person with diabetesobtains a blood sample and measures the glucose concentration using atest strip, or the like. Unfortunately, this information does not tellthe person with diabetes whether the blood glucose concentration isrising or falling. Rising or falling directional trend information canbe particularly important in a situation such as illustrated in FIG. 42,wherein if the user does not know that the glucose concentration isrising, he/she may assume that the glucose concentration is falling andnot attend to his/her condition. However, because rising directionaltrend information 518 is provided, the person with diabetes can preemptthe clinical risk by attending to his/her condition (for example,administer insulin). Estimated analyte data can be incorporated into thedirectional trend information by characteristics of the arrow, forexample, size, color, flash speed, or the like.

In some embodiments, the gradient bar can be a vertical instead ofhorizontal bar. In some embodiments, a gradient fill can be used torepresent analyte concentration, variation, or clinical risk, forexample. In some embodiments, the bar graph includes color, for examplethe center can be green in the safe zone that graduates to red in thedanger zones; this can be in addition to or in place of the dividedsegments. In some embodiments, the segments of the bar graph are clearlydivided by lines; however color, gradation, or the like can be used torepresent areas of the bar graph. In some embodiments, the directionalarrow can be represented by a cascading level of arrows to a representslow or rapid rate of change. In some embodiments, the directional arrowcan be flashing to represent movement or pending danger.

The screen 170 of FIG. 42 can further comprise a numericalrepresentation of analyte concentration, date, time, or otherinformation to be communicated to the patient. However, a user canadvantageously extrapolate information helpful for his/her conditionusing the simple and effective representation of this embodiment shownin FIG. 42, without reading a numeric representation of his/her analyteconcentration.

In some alternative embodiments, a trend graph or gradient bar, a dial,pie chart, or other visual representation can provide analyte data usingshading, colors, patterns, icons, animation, or the like.

FIG. 43 is an illustration of a receiver in one embodiment, whichincludes measured analyte values and a target analyte value(s). FIG. 44is an illustration of the receiver of 22 further including estimatedanalyte values. FIG. 45 is an illustration of the receiver of 23 furtherincluding variations of estimated analyte values and including therapyrecommendations to aid a user in obtaining the target analyte value.

FIG. 43 is an illustration of the receiver 158 in one embodiment,wherein the screen 170 shows measured analyte values 520 and one (ormore) clinically acceptable target analyte values 522. The measuredanalyte values 520 are illustrated as a trend graph, such as describedwith reference to FIG. 41, however other representations are alsopossible.

Additionally, one or more clinically acceptable target analyte values522 are provided as output, for example such as shown in FIG. 43. Insome embodiments, the clinically acceptable target analyte values can beobtained from a variation analysis of clinical, physiological, orstatistical variation, such as described in more detail elsewhereherein. Namely, the variation analysis provides the analyzed variationof the estimated analyte values, and the output module 18 (or processor16) further analyzes the variation of estimated analyte values for thosethat are clinically acceptable and optionally also ensures physiologicalfeasibility. For example, analysis of clinical risk can visually directa patient to aim for an analyte value in a safe zone (for example,outside of the clinically risky zone).

In some embodiments, the output displays a point representing a targetanalyte value. In some embodiments, the output displays an objectrepresenting a general target analyte area. In some embodiments, theoutput displays a path of target analyte values. In some embodiments,the output displays a range of target analyte values along that path.

Humans are generally particularly responsive to targets, namely, able tounderstand the intention of targets and desire to obtain them.Advantageously, the output of target analyte values provides a goaltowards which the user will aim. In the example shown on FIG. 41, themeasured analyte values 520 indicate an upward trend of analyteconcentration, and a user can likely visualize that the trend of themeasured analyte values 520 will not likely hit the target 522 withoutintervention or action. Therefore, a user will be prompted toproactively care for his/her analyte concentration in an effort to hitthe target analyte value(s) 522 (for example, administer insulin).

In some embodiments, the manufacturer, physician, patient, computerprogram, or the like can set the target analyte values. In someembodiments, a physician can set static target analyte values based onage, time of day, meal time, severity of medical condition, or the like;in such embodiments, the targets can be regularly or intermittentlydisplayed in an effort to modify patient behavior through habitualreminders and training. Targets can be continually maintained on thescreen or selectively displayed, for example when clinical risk isestimated, but can be avoided. In some embodiments, the target valuescan be dynamic targets, namely, targets that are dependent upon variableparameters such as age, time of day, meal time, severity of medicalcondition, medications received (for example, insulin injections) or thelike, which can be input by a user or external device.

In one example of targets useful for a person with diabetes monitoringglucose concentration, the target glucose levels for a person withdiabetes are typically between about 80 and about 130 mg/dL before mealsand less than about 180 mg/dL one to two hours after a meal. In anotherexemplary embodiment, the amount and timing of insulin injections can beconsidered in determining the estimation of and target glucose rangesfor a person with diabetes.

FIG. 44 is an illustration of the receiver 158 in another embodimentshowing the measured analyte values 520 and clinically acceptable targetanalyte value(s) 522 of FIG. 43 and further showing estimated analytevalues 524 on the same screen. In some embodiments, the data can beseparated onto different screens that can be selectively viewed.However, viewing both estimated analyte values and the target analytevalues can be useful in educating the patient regarding control ofhis/her analyte levels, since estimated and target analyte values arephysiologically feasible in view of known physiological parametersdescribed elsewhere herein. Estimated analyte values can be calculatedand displayed in any manner described in the preferred embodiments.

FIG. 45 is an illustration of a receiver in another embodiment,including measured analyte values 520, target analyte values 522,estimated analyte values 524, such as described in more detail abovewith reference to FIGS. 43 and 44, and further including variations ofestimated analyte values 526 and therapy recommendations 528 on thescreen to help the user obtain the displayed target analyte values 522.The variations of estimated analyte values are calculated such asdescribed in more detail with reference to FIG. 36.

The target analyte values presented should be physiologically feasible;therefore, type and/or amount of therapy can be determined (orestimated) to aid the patient in obtaining those therapy goals. In someembodiments, the therapy recommendations are representative icons, suchas the injection icon 528 shown in FIG. 45. In alternative embodiments,icons can include an apple, orange juice, candy bar, or any iconrepresentative of eating, drinking, or medicating, for example. In someembodiments, the therapy recommendations are preset alphanumericmessages (for example, “consume carbohydrates”, “inject insulin”, or “notherapy required”). In some embodiments therapy recommendations can becustomized (for example, by a manufacturer, physician, patient, computerprogram, and/or the like) in order to provide more reliable, accurate,clinically safe, and/or individualized goals. For example, a physiciancan input information helpful in determining therapy recommendationsusing individual physiological considerations. As another example, datacan be input via the user interface or via a wired or wirelessconnection to the receiver, such as age, time of day, meal time,severity of medical condition, medications received (for example,insulin injections) or the like, which can be used to determine theappropriate therapy recommendations.

In some embodiments, the therapy recommendations include a variety ofscenarios, which the viewer can view and/or select. In theseembodiments, the patient is given more control and able to makedecisions based that fits best with their lifestyle or presentcircumstance, or considering external influences of which the system wasunaware.

In some embodiments, therapy recommendations are sent to an externaldevice (for example, insulin delivery mechanism), which is described inmore detail with reference to FIGS. 48 to 51.

FIGS. 46 and 47 are views of the receiver showing an analyte trendgraph, including measured analyte values and dynamic visualrepresentation of range of estimated analyte values based on a variationanalysis, such as described in more detail with reference to FIG. 36.

FIG. 46 is an illustration of a receiver 158 in another embodiment,including a screen 170 that shows the measured analyte values 530 and avariation of estimated analyte values 532 in one exemplary embodiment.In this embodiment, the visual representation of the variation ofestimated analyte values 532 includes exemplary paths representative ofthe analyzed variation of estimated analyte values that illustrates arange of possible future analyte values. In some embodiments, thevariation of estimated analyte values 532 is represented by a shape thatbegins at the most recently measured analyte value 534 and includesboundaries 536 that represent the range of possible variations ofestimated analyte values for a future time period. The shape can bestatic or dynamic depending on the type of variation analyzed by theestimative algorithm, for example a fan, teardrop, or other shapedobject.

FIG. 47 is an illustration of a receiver 158 in another embodiment,including a screen 170 that shows the measured analyte values 538 and avariation of estimated analyte values 540 in another exemplaryembodiment. In this embodiment, the variation can include an estimatedpath and boundaries, for example, which can be obtained from a variationanalysis and/or from physiological parameters, for example. In somealternative embodiments, color or other illustrative representation oflevels of safety or danger can be provided on the screen.

FIG. 48 is an illustration of a receiver 158 in another embodiment,including a screen 170 that shows a numerical representation of the mostrecent measured analyte value 542. This numerical value 542 ispreferably a calibrated analyte value, such as described in more detailelsewhere herein. Additionally, this embodiment preferably provides anarrow 544 on the screen 170, which represents the rate of change of thehost's analyte concentration. A bold “up” arrow is shown on the drawing,which preferably represents a relatively quickly increasing rate ofchange. The arrows shown with dotted lines illustrate examples of otherdirectional arrows (for example, rotated by 45 degrees), which can beuseful on the screen to represent various other positive and negativerates of change. Although the directional arrows shown have a relativelow resolution (45 degrees of accuracy), other arrows can be rotatedwith a high resolution of accuracy (for example one degree of accuracy)to more accurately represent the rate of change of the host's analyteconcentration. In some alternative embodiments, the screen provides anindication of the acceleration of the host's analyte concentration.

A second numerical value 546 is shown, which is representative of avariation of the measured analyte value 542. The second numerical valueis preferable determined from a variation analysis based on statistical,clinical, or physiological parameters, such as described in more detailelsewhere herein. In one embodiment, the second numerical value 546 isdetermined based on clinical risk (for example, weighted for thegreatest possible clinical risk to a patient). In another embodiment,the second numerical representation 546 is an estimated analyte valueextrapolated to compensate for a time lag, such as described in moredetail elsewhere herein. In some alternative embodiments, the receiverdisplays a range of numerical analyte values that best represents thehost's estimated analyte value (for example, +/−10%). In someembodiments, the range is weighted based on clinical risk to thepatient. In some embodiments, the range is representative of aconfidence in the estimated analyte value and/or a variation of thosevalues. In some embodiments, the range is adjustable.

Patient Display

The potential of continuous glucose monitoring as an aid to bothdiabetic patients and their caregivers is well recognized. For thepatient, continuous monitoring provides hour-to-hour glucose informationthat enables intensive therapy: it can be used to reduce the extent ofhyperglycemic excursions without increasing the risk of hypoglycemicevents. For caregivers of patients with diabetes, continuous monitoringprovides day-to-day glucose information that can be used to optimizetherapy. Despite these differences in purpose/perspective (hour-to-hourdata for the patient, day-to-day information for the caregiver), theconventional display of continuous glucose data has heretofore not beenadapted to the intended use/user. Accordingly, continuous glucosedisplay methods that are utility-driven, and that allow the data to beeasily perceived and interpreted is desirable.

Glucose data are typically displayed on a graph with y-axis that spans aphysiologic range of glucose (e.g. 40-400 mg/dl) and is uniform, i.e.the distance on the graph between 60 and 80 mg/dl is the same as thedistance between 160 and 180 mg/dl, even though the clinical meanings ofthese two differences are significantly different. An alternativedisplay uses a non-uniform y-axis that makes differences at low glucoselevels easier to perceive. The difference in appearance of these twographs is depicted in FIG. 49, which illustrates the conventionaldisplay of a 9-hour trend graph; FIG. 50 illustrates a display with ay-axis that has been equally divided into three zones (low, medium, andhigh glucose) though the glucose range (max−min) of each zone isdifferent (40-90 mg/dl, 90-180 mg/dl, 180-400 mg/dl). The non-uniformy-axis in FIG. 50 appears to cause distortion to the glucose trend butdoes not appear to be misleading. More importantly, the dynamics at lowglucose are more easily perceived in FIG. 50 than in FIG. 49.

Physicians use continuous glucose monitoring primarily for therapyoptimization. Though the hour-to-hour dynamics of glucose can containinformation related to therapy adjustment, a longer-term/summaryperspective is perhaps easier perceive and interpret, and morereflective of changes in a patient's glycemic control. In this way,physician monitoring of a patient's glycemic control is similar toprocess monitoring used in quality control of manufactured products: theaim of both is to rapidly detect when the system/process is in or out ofcontrol, or to detect trends that can indicate changes in control.Control charts, which plot averages and ranges of process parametersover time, are a well-established and powerful illustration of processcontrol and can be applicable to continuous glucose monitoring. FIGS. 51and 52 illustrate the difference in how well the data reflect changes inglycemic control. FIG. 51 is a conventional plot of glucose over oneweek; FIG. 52 is a plot of the 24-hour (12 AM-12 AM) median(+/−interquartile range) glucose.

The display provides improved utility of continuous glucose data,enabling improved clinical outcomes, and offers advantages over priorart displays wherein the display of continuous glucose data is nottailored to the intended use.

FIG. 53 is an illustration of a receiver that interfaces with acomputer. A receiver 158 is provided that is capable of communicationwith a computer 580. The communication can include one-way or two-waywired or wireless transmissions 582. The computer 580 can be any systemthat processes information, such as a PC, server, personal digitalassistant (PDA), or the like.

In some embodiments, the receiver sends information to the computer, forexample, measured analyte data, estimated analyte data, target analytedata, therapy recommendations, or the like. The computer can includesoftware that processes the data in any manner known in the art.

In some embodiments, the computer sends information to the receiver; forexample, updating software, customizing the receiver programming (forexample, setting individualized parameters), providing real timeinformation (for example, mealtime and exercise that has been enteredinto a PDA), or the like.

FIG. 54 is an illustration of a receiver 158 that interfaces with amodem 590, wherein data is transmitted via wireless transmissions 592between the receiver and a modem in order to interface with atelecommunications line (for example, phone, pager, internet, network,etc). By providing an interface with a telecommunications line, thereceiver can send and receive information from parties remote from thereceiver, such as at a hospital, doctor's office, caretaker's computer,nationally-based server, or the like.

In some embodiments, the modem allows the receiver to send emergencymessages to an emergency contact, such as a family member, hospital,Public Safety Answering Point (PSAP), or the like when analyteconcentration are in a zone of extreme clinical risk. In someembodiments, a patient's doctor monitors his/her analyte concentrationremotely and is able to request an appointment when certain conditionsare not being met with the patient's analyte concentration. Numerousother uses can be contrived for communicating information via a modem590 between the receiver 158 and another party, all of which areencompassed in the preferred embodiments.

FIG. 55 is an illustration of a receiver 158 that interfaces with aninsulin pen 600, wherein data is transmitted via wireless transmission602 between the receiver and the insulin pen 600. In some embodiments,the receiver sends therapy recommendations to the insulin pen, such asamount and time of insulin injection. In some embodiments, the insulinpen sends amount of therapy administered by a patient, such as type,amount, and time of administration. Such information can be used in dataanalysis, including estimation of analyte values, output of therapyrecommendations, and trend analysis, for example.

FIG. 56 is an illustration of a receiver 158 that interfaces with aninsulin pump 610, wherein data is transmitted via wireless transmission612 between the receiver 158 and the insulin pump 610. In someembodiments, the receiver sends therapy recommendations to the insulinpump 610, such as amount and time of insulin administration. In someembodiments, the insulin pump 610 sends information regarding therapy tobe administered such as type, amount, and time of administration. Suchinformation can be used in data analysis, including estimation ofanalyte values, output of therapy recommendations, and trend analysis,for example.

In general, any of the above methods of data input and output can becombined, modified, selectively viewed, selectively applied, orotherwise altered without departing from the scope of the presentinvention.

Methods and devices that are suitable for use in conjunction withaspects of the preferred embodiments are disclosed in U.S. Pat. Nos.4,994,167; 4,757,022; 6,001,067; 6,741,877; 6,702,857; 6,558,321;6,931,327; 6,862,465; 7,074,307; 7,081,195; 7,108,778; 7,110,803; andU.S. Pat. No. 7,192,450.

Methods and devices that are suitable for use in conjunction withaspects of the preferred embodiments are disclosed in U.S. PatentPublication No. US-2005-0176136-A1; U.S. Patent Publication No.US-2005-0251083-A1; U.S. Patent Publication No. US-2005-0143635-A1; U.S.Patent Publication No. US-2005-0181012-A1; U.S. Patent Publication No.US-2005-0177036-A1; U.S. Patent Publication No. US-2005-0124873-A1; U.S.Patent Publication No. US-2005-0115832-A1; U.S. Patent Publication No.US-2005-0245799-A1; U.S. Patent Publication No. US-2005-0245795-A1; U.S.Patent Publication No. US-2005-0242479-A1; U.S. Patent Publication No.US-2005-0182451-A1; U.S. Patent Publication No. US-2005-0056552-A1; U.S.Patent Publication No. US-2005-0192557-A1; U.S. Patent Publication No.US-2005-0154271-A1; U.S. Patent Publication No. US-2004-0199059-A1; U.S.Patent Publication No. US-2005-0054909-A1; U.S. Patent Publication No.US-2005-0051427-A1; U.S. Patent Publication No. US-2003-0032874-A1; U.S.Patent Publication No. US-2005-0103625-A1; U.S. Patent Publication No.US-2005-0203360-A1; U.S. Patent Publication No. US-2005-0090607-A1; U.S.Patent Publication No. US-2005-0187720-A1; U.S. Patent Publication No.US-2005-0161346-A1; U.S. Patent Publication No. US-2006-0015020-A1; U.S.Patent Publication No. US-2005-0043598-A1; U.S. Patent Publication No.US-2003-0217966-A1; U.S. Patent Publication No. US-2005-0033132-A1; U.S.Patent Publication No. US-2005-0031689-A1; U.S. Patent Publication No.US-2004-0186362-A1; U.S. Patent Publication No. US-2005-0027463-A1; U.S.Patent Publication No. US-2005-0027181-A1; U.S. Patent Publication No.US-2005-0027180-A1; U.S. Patent Publication No. US-2006-0020187-A1; U.S.Patent Publication No. US-2006-0036142-A1; U.S. Patent Publication No.US-2006-0020192-A1; U.S. Patent Publication No. US-2006-0036143-A1; U.S.Patent Publication No. US-2006-0036140-A1; U.S. Patent Publication No.US-2006-0019327-A1; U.S. Patent Publication No. US-2006-0020186-A1; U.S.Patent Publication No. US-2006-0020189-A1; U.S. Patent Publication No.US-2006-0036139-A1; U.S. Patent Publication No. US-2006-0020191-A1; U.S.Patent Publication No. US-2006-0020188-A1; U.S. Patent Publication No.US-2006-0036141-A1; U.S. Patent Publication No. US-2006-0020190-A1; U.S.Patent Publication No. US-2006-0036145-A1; U.S. Patent Publication No.US-2006-0036144-A1; U.S. Patent Publication No. US-2006-0016700-A1; U.S.Patent Publication No. US-2006-0142651-A1; U.S. Patent Publication No.US-2006-0086624-A1; U.S. Patent Publication No. US-2006-0068208-A1; U.S.Patent Publication No. US-2006-0040402-A1; U.S. Patent Publication No.US-2006-0036142-A1; U.S. Patent Publication No. US-2006-0036141-A1; U.S.Patent Publication No. US-2006-0036143-A1; U.S. Patent Publication No.US-2006-0036140-A1; U.S. Patent Publication No. US-2006-0036139-A1; U.S.Patent Publication No. US-2006-0142651-A1; U.S. Patent Publication No.US-2006-0036145-A1; U.S. Patent Publication No. US-2006-0036144-A1; U.S.Patent Publication No. US-2006-0200022-A1; U.S. Patent Publication No.US-2006-0198864-A1; U.S. Patent Publication No. US-2006-0200019-A1; U.S.Patent Publication No. US-2006-0189856-A1; U.S. Patent Publication No.US-2006-0200020-A1; U.S. Patent Publication No. US-2006-0200970-A1; U.S.Patent Publication No. US-2006-0183984-A1; U.S. Patent Publication No.US-2006-0183985-A1; U.S. Patent Publication No. US-2006-0195029-A1; U.S.Patent Publication No. US-2006-0229512-A1; U.S. Patent Publication No.US-2006-0222566-A1; U.S. Patent Publication No. US-2007-0032706-A1; U.S.Patent Publication No. US-2007-0016381-A1; U.S. Patent Publication No.US-2007-0027370-A1; U.S. Patent Publication No. US-2007-0027384-A1; U.S.Patent Publication No. US-2007-0032717-A1; U.S. Patent Publication No.US-2007-0032718 A1; U.S. Patent Publication No. US-2007-0059196-A1; andU.S. Patent Publication No. US-2007-0066873-A1.

Methods and devices that are suitable for use in conjunction withaspects of the preferred embodiments are disclosed in U.S. applicationSer. No. 09/447,227 filed Nov. 22, 1999 and entitled “DEVICE AND METHODFOR DETERMINING ANALYTE LEVELS”; U.S. application Ser. No. 11/654,135filed Jan. 17, 2007 and entitled “POROUS MEMBRANES FOR USE WITHIMPLANTABLE DEVICES”; U.S. application Ser. No. 11/675,063 filed Feb.14, 2007 and entitled “ANALYTE SENSOR”; U.S. application Ser. No.11/543,734 filed Oct. 4, 2006 and entitled “DUAL ELECTRODE SYSTEM FOR ACONTINUOUS ANALYTE SENSOR”; U.S. application Ser. No. 11/654,140 filedJan. 17, 2007 and entitled “MEMBRANES FOR AN ANALYTE SENSOR”; U.S.application Ser. No. 11/654,327 filed Jan. 17, 2007 and entitled“MEMBRANES FOR AN ANALYTE SENSOR”;U.S. application Ser. No. 11/543,396filed Oct. 4, 2006 and entitled “ANALYTE SENSOR”; U.S. application Ser.No. 11/543,490 filed Oct. 4, 2006 and entitled “ANALYTE SENSOR”; U.S.application Ser. No. 11/543,404 filed Oct. 4, 2006 and entitled “ANALYTESENSOR”; U.S. application Ser. No. 11/681,145 filed Mar. 1, 2007 andentitled “ANALYTE SENSOR”; U.S. application Ser. No. 11/690,752 filedMar. 23, 2007 and entitled “TRANSCUTANEOUS ANALYTE SENSOR”; U.S.application Ser. No. 11/691,426 filed Mar. 26, 2007 and entitled“ANALYTE SENSOR”; U.S. application Ser. No. 11/691,432 filed Mar. 26,2007 and entitled “ANALYTE SENSOR”; U.S. application Ser. No. 11/691,424filed Mar. 26, 2007 and entitled “ANALYTE SENSOR”; U.S. application Ser.No. 11/691,466 filed Mar. 26, 2007 and entitled “ANALYTE SENSOR”; andU.S. application Ser. No. 11/692,154 filed Mar. 27, 2007 and entitled“DUAL ELECTRODE SYSTEM FOR A CONTINUOUS ANALYTE SENSOR”.

All references cited herein, including but not limited to published andunpublished applications, patents, and literature references, areincorporated herein by reference in their entirety and are hereby made apart of this specification. To the extent publications and patents orpatent applications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

The term “comprising” as used herein is synonymous with “including,”“containing,” or “characterized by,” and is inclusive or open-ended anddoes not exclude additional, unrecited elements or method steps.

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term “about.” Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

The above description discloses several methods and materials of thepresent invention. This invention is susceptible to modifications in themethods and materials, as well as alterations in the fabrication methodsand equipment. Such modifications will become apparent to those skilledin the art from a consideration of this disclosure or practice of theinvention disclosed herein. Consequently, it is not intended that thisinvention be limited to the specific embodiments disclosed herein, butthat it cover all modifications and alternatives coming within the truescope and spirit of the invention.

1. A system for monitoring a glucose concentration in a host, the system comprising: a continuous glucose sensor configured to produce a signal indicative of a glucose concentration in a host; monitoring system electronics operably connected to the sensor and configured to receive reference data from a single point glucose measuring device, the single point glucose measuring device configured to receive a biological sample from the host and to measure a concentration of glucose in the biological sample, wherein the reference data is indicative of a measured glucose concentration in the biological sample, wherein the monitoring system electronics are configured to provide real-time calibrated glucose concentration values of the host based on the signal, and wherein providing the real-time calibrated glucose concentration values comprises evaluating an acceptability of the reference data in real time using a clinical acceptability grid, and calibrating or confirming the signal responsive to the acceptability evaluation of the reference data.
 2. The system of claim 1, wherein the monitoring system electronics comprise a user interface, and wherein the monitoring system electronics are further configured to display real-time glucose concentration information on the user interface based on the real-time calibrated glucose concentration values.
 3. The system of claim 1, wherein the system further comprises a receiver and wherein the monitoring system electronics are housed within the receiver.
 4. The system of claim 3, wherein the reference glucose measuring device is built into the receiver, the single point glucose measuring device having a port configured to accept the biological sample.
 5. The system of claim 3, wherein the reference glucose measuring device is separate from the receiver and wherein the receiver includes a user interface configured to accept manual input from a user indicative of a value of the reference data.
 6. The system of claim 1, wherein the clinical acceptability grid is one of a Clarke Error grid and a Consensus grid.
 7. The system of claim 1, wherein the clinical acceptability grid provides an indication of a clinical acceptability of a disparity between a reference value of the reference data and a sensor value of the signal.
 8. The system of claim 7, wherein the reference value and the sensor value are in units of glucose concentration.
 9. The system of claim 1, wherein the monitoring system electronics are configured to calibrate or confirm the signal using the reference data only if the acceptability module determines that the reference data is acceptable.
 10. A system comprising one or more processors and computer readable memory, the computer readable memory comprising code that, when executed by the one or more processors, causes the one or more processors to: process a signal received from a continuous glucose measuring device; measure a concentration of glucose in a biological sample received from a host, the measured glucose concentration in the sample comprising reference data; and provide real-time calibrated glucose concentration values of the host based on the signal, wherein providing the real-time calibrated glucose concentration values comprises evaluating an acceptability of the reference data in real time using a clinical acceptability grid, and calibrating or confirming the signal responsive to the acceptability evaluation of the reference data.
 11. The system of claim 10, wherein the system further comprises a receiver and wherein the one or more processors and computer-readable memory are housed within the receiver.
 12. The system of claim 11, wherein the reference glucose measuring device is built into the receiver, the single point glucose measuring device having a port configured to accept the biological sample.
 13. The system of claim 11, wherein the reference glucose measuring device is separate from the receiver and wherein the receiver includes a user interface configured to accept manual input from a user indicative of a value of the reference data.
 14. The system of claim 10, wherein the clinical acceptability grid is one of a Clarke Error grid and a Consensus grid.
 15. The system of claim 10, wherein the clinical acceptability grid provides an indication of a clinical acceptability of a disparity between a reference value of the reference data and a sensor value of the signal.
 16. The system of claim 15, wherein the reference value and the sensor value are in units of glucose concentration.
 17. The system of claim 10, wherein the code causes the one or more processors to calibrate or confirm the signal using the reference data only if the reference data is determined to be acceptable.
 18. The system of claim 10, wherein the code additionally causes, when executed by the one or more processors, the one or more processors to display information indicative of the real-time calibrated glucose concentration values on a user interface of the system.
 19. A method for monitoring glucose concentration in a host, the method comprising: generating a signal from a continuous glucose measuring device indicative of a glucose concentration in a host; measuring a concentration of glucose in a biological sample in a single point glucose measuring device, the measured glucose concentration in the biological sample comprising reference data; providing, using monitoring system electronics of a monitoring system, real-time calibrated glucose concentration values of the host based on the signal, wherein providing the real-time calibrated glucose concentration values comprises evaluating an acceptability of the reference data in real time using a clinical acceptability grid and calibrating or confirming the signal responsive to the acceptability evaluation of the reference data.
 20. The method of claim 19, wherein the monitoring system further comprises a receiver and wherein the monitoring system electronics are housed within the receiver.
 21. The method of claim 20, wherein the single point glucose measuring device is built into the receiver, the single point glucose measuring device having a port configured to accept the biological sample.
 22. The method of claim 20, wherein the reference glucose measuring device is separate from the receiver and wherein the method further comprises receiving manual input using the receiver indicative of a value of the reference data.
 23. The method of claim 19, wherein the clinical acceptability grid is one of a Clarke Error grid and a Consensus grid.
 24. The method of claim 19, wherein the clinical acceptability grid provides an indication of a clinical acceptability of a disparity between a reference value of the reference data and a sensor value of the signal.
 25. The method of claim 24, wherein the reference value and the sensor value are in units of glucose concentration.
 26. The method of claim 19, wherein calibrating or confirming the signal only uses reference data that is determined to be acceptable in the acceptability evaluation. 